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CLEP College Level Examination Program (All CLEP Exam Pack)

The CLEP College Composition exam assesses writing skills taught in most first-year college composition courses. Those skills include analysis, argumentation, synthesis, usage, ability to recognize logical development, and research.



The College Composition exam contains approximately 50 multiple-choice questions to be answered in approximately 50 minutes and 2 mandatory, centrally scored essays to be written in 70 minutes, for a total testing time of 120 minutes. The essays are scored twice a month by college English faculty from throughout the country via an online scoring system. Each of the two essays is scored independently by at least two different readers, and the scores are then combined. This combined score is weighted approximately equally with the score from the multiple-choice section. These scores are then combined to yield the test-takers score. The resulting combined score is reported as a single scaled score between 20 and 80. Separate scores are not reported for the multiple-choice and essay sections.



Note: Although scores are provided immediately upon completion for other CLEP exams, scores for the College Composition exam are available to test takers one to two weeks after the test date. View the complete College Composition Scoring and Score Availability Dates.



The exam includes some pretest multiple-choice questions that won't be counted toward the test taker's score.



Colleges set their own credit-granting policies and therefore differ with regard to their acceptance of the College Composition exam. Most colleges will grant course credit for a first-year composition or English course that emphasizes expository writing; others will grant credit toward satisfying a liberal arts or distribution requirement in English.



The American Council on Educations College Credit Recommendation Service (ACE CREDIT) has evaluated the exam and recommended the awarding of college credit for a score of 50 or above on the CLEP College Composition exam.



Conventions of Standard Written English (10%)

This section measures test takers' awareness of a variety of logical, structural, and grammatical relationships within sentences. The questions test recognition of acceptable usage relating to the items below:



Syntax (parallelism, coordination, subordination)

Sentence boundaries (comma splices, run-ons, sentence fragments)

Recognition of correct sentences

Concord/agreement (pronoun reference, case shift, and number; subject-verb; verb tense)

Diction

Modifiers

Idiom

Active/passive voice

Lack of subject in modifying word group

Logical comparison

Logical agreement

Punctuation


Revision Skills (40%)

This section measures test takers' revision skills in the context of works in progress (early drafts of essays):



Organization

Evaluation of evidence

Awareness of audience, tone, and purpose

Level of detail

Coherence between sentences and paragraphs

Sentence variety and structure

Main idea, thesis statements, and topic sentences

Rhetorical effects and emphasis

Use of language

Evaluation of author's authority and appeal

Evaluation of reasoning

Consistency of point of view

Transitions

Sentence-level errors primarily relating to the conventions of Standard Written English


Ability to Use Source Materials (25%)

This section measures test takers' familiarity with elements of the following basic reference and research skills, which are tested primarily in sets but may also be tested through stand-alone questions. In the passage-based sets, the elements listed under Revision Skills and Rhetorical Analysis may also be tested. In addition, this section will cover the following skills:



Use of reference materials

Evaluation of sources

Integration of resource material

Documentation of sources (including, but not limited to, MLA, APA, and Chicago manuals of style)


Rhetorical Analysis (25%)

This section measures test takers' ability to analyze writing. This skill is tested primarily in passage-based questions
pertaining to critical thinking, style, purpose, audience, and situation:



Appeals

Tone

Organization/structure

Rhetorical effects

Use of language

Evaluation of evidence

The Essays

In addition to the multiple-choice section, the College Composition exam includes a mandatory essay section that tests skills of argumentation, analysis, and synthesis. This section of the exam consists of two essays, both of which measure a test taker's ability to write clearly and effectively. The first essay is based on the test taker's reading, observation, or experience, while the second requires test takers to synthesize and cite two sources that are provided. Test takers have 30 minutes to write the first essay and 40 minutes to read the two sources and write the second essay. The essays must be typed on the computer.



First Essay: Directions

Write an essay in which you discuss the extent to which you agree or disagree with the statement provided. Support your discussion with specific reasons and examples from your reading, experience, or observations.



Second Essay: Directions

This assignment requires you to write a coherent essay in which you synthesize the two sources provided. Synthesis refers to combining the sources and your position to form a cohesive, supported argument. You must develop a position and incorporate both sources. You must cite the sources whether you are paraphrasing or quoting. Refer to each source by the authors last name, the title, or by any other means that adequately identifies it.



Essay Scoring Guidelines

Readers will assign scores based on the following scoring guide.



6 – A 6 essay demonstrates a high degree of competence and sustained control, although it may have a few minor errors.


A typical essay in this category


addresses the writing task very effectively

develops ideas thoroughly, using well-chosen reasons, examples, or details for support

is clearly-focused and well-organized

demonstrates superior facility with language, using effective vocabulary and sentence variety

demonstrates strong control of the standard conventions of grammar, usage, and mechanics, though it may contain minor errors
5 – A 5 essay demonstrates a generally high degree of competence, although it will have occasional lapses in quality.


A typical essay in this category:


addresses the writing task effectively

develops ideas consistently, using appropriate reasons, examples, or details for support

is focused and organized

demonstrates facility with language, using appropriate vocabulary and some sentence variety

demonstrates consistent control of the standard conventions of grammar, usage, and mechanics, though it may contain minor errors
4 – A 4 essay demonstrates competence, with some errors and lapses in quality.


A typical essay in this category


addresses the writing task adequately

develops ideas adequately, using generally relevant reasons, examples, or details for support

is generally focused and organized

demonstrates competence with language, using adequate vocabulary and minimal sentence variety

demonstrates adequate control of the standard conventions of grammar, usage, and mechanics; errors do not interfere with meaning
3 – A 3 essay demonstrates limited competence.


A typical essay in this category exhibits one or more of the following weaknesses:


addresses the writing task, but may fail to sustain a focus or viewpoint

develops ideas unevenly, often using assertions rather than relevant reasons, examples, or details for support is poorly focused and/or poorly organized displays frequent problems in the use of language, using unvaried diction and syntax
demonstrates some control of grammar, usage, and mechanics, but with occasional shifts and inconsistencies

2 – A 2 essay is seriously flawed.


A typical essay in this category exhibits one or more of the following weaknesses:


addresses the writing task in a seriously limited or unclear manner

develops ideas thinly, providing few or no relevant reasons, examples, or details for support is unfocused and/or disorganized
displays frequent serious language errors that may interfere with meaning

demonstrates a lack of control of standard grammar, usage, and mechanics

1 – A 1 essay is fundamentally deficient.


A typical essay in this category exhibits one or more of the following weaknesses:


does not address the writing task in a meaningful way

does not develop ideas with relevant reasons, examples, or details

displays a fundamental lack of control of language that may seriously interfere with meaning

0 – Off topic.


Provides no evidence of an attempt to respond to the assigned topic, is written in a language other than English, merely copies the prompt, or consists of only keystroke characters.
* For the purposes of scoring, synthesis refers to combining the sources and the writers position to form a cohesive, supported argument.
College Level Examination Program (All CLEP Exam Pack)
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CLEP College Level Examination Program (All CLEP Exam Pack)

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Answer: B
Question: 600
Which of the following industries did not experience major growth from the late 1800s to the
early 1900s?
A. Beef
B. Steel
C. Oil
D. Cloth
E. Wood
Answer: A
Question: 601
Which statement does not accurately portray changes to the American labor force wrought by
industrialization in the late 19th and early 20th centuries?
A. By 1880, there were almost five million individuals working in industrial jobs.
B. More than two million women were employed in factories or offices by 1880.
C. By the year 1890, 25% of children aged 10 to 15 years were in the workforce.
D. During these years it was common for workers to sustain injuries and illness.
E. Workers' compensation, disability, and retirement did not exist for workers.
Answer: C
Question: 602
Which of the following is true regarding business consolidation around the turn of the 19th
and20th centuries in American industries?
A. John D Rockefeller used vertical integration with his Standard Oil Company.
B. Gustavus Swift was the first to use horizontal integration with meat packing.
C. Social Darwinists were opposed to consolidation as well as to deregulation.
D. Economists against consolidation favored more control of natural processes.
E. The Sherman Anti-Trust Act proved quite effective in preventing monopoly.
Answer: D
Question: 603
Which of the following statistics is not accurate with respect to the urbanization of America
between 1870 and 1920?
A. The number of Americans who lived in cities grew more than fivefold during this time.
B. By 1920, almost half of the American population lived in cities with over 2500 people.
C. Cities having populations greater than 100 000 went up by more than 3.5 times.
D. Cities having populations greater than 500 000 increased to six-fold.
Answer: B
Question: 604
Around the turn of the 19th and 20th century, when city slums resulted from overcrowding due
to immigration, in which of the following areas was the least improvement made?
A. Housing
B. Sewage
C. Firefighting
D. Street lights
E. Water supply
Answer: A
Question: 605
Which of the following was not a development of city culture in America around the turn of the
19th and20th century?
A. Baseball had become America's favorite professional sport by the 1880s.
B. Vaudeville shows rode trains from cities to small towns across America.
C. Early directors liked W Griffith developed the motion picture industry.
D. Pulitzer and Hearst gave mass popularity to newspapers and magazines.
E. All of these were American urban cultural developments during this time.
Answer: E
Section 48: Sec Forty Eight (606 to 614)
Details:High Pressure Chemistry Practice
Question: 606
What is the pressure of a mixture of CO 2, SO 2 and H 20 gases, if each gas has a partial pressure of
250 torr?
A. 250 torr
B. 500 torr
C. 750 torr
D. 1,000 torr
Answer: C
Question: 607
A gas sample has a volume of 25.0 milliliters at a pressure of 1.0 atmosphere. If the volume
increases to 50.0 milliliters and the temperature remains constant, the new pressure will be
A. 1.0 atm
B. 2.0 atm
C. .250 atm
D. .500 atm
Answer: D
Question: 608
An increase of the temperature of a system at equilibrium favors the
A. Endothermic reaction and decreases its rate
B. Endothermic reaction and increases its rate
C. Exothermic reaction and decreases its rate
D. Exothermic reaction and increases its rate
Answer: B
Question: 609
oxygen gas in a closed system has a volume of 200 milliliters at 600 k. If the
A sample of
pressure is held constant and the temperature is lowered to 300 K, the volume of the gas will be
A. 100 ml
B. 200 ml
C. 300 ml
D. 400 ml
Answer: A
Question: 610
An assumption of the kinetic energy of gases is that the particles of a gas have
A. Little attraction for each other and a significant volume
B. Little attraction for each other and an insignificant volume
C. Strong attraction for each other and a significant volume
D. Strong attraction for each other and an insignificant volume
Answer: B
Question: 611
What is the volume occupied by 2.0 moles of Ar(g) at STP?
A. 22.4 L
B. 44.8 L
C. 89.6 L
D. 179 L
Answer: B
Question: 612
Which gas is least likely to obey the ideal gas laws at very high temperatures and very low
temperatures?
A. Kr
B. Ne
C. He
D. Xe
Answer: D
Question: 613
A gas at STP has a volume of 1.0 liters. If the pressure is doubled and the temperature remains
constant, the new volume of the gas will be
A. .25 L
B. 2.0 L
C. .50 L
D. 4.0 L
Answer: C
Question: 614
At 1 atmosphere of pressure, 25.0 grams of a compound at its boiling point is converted to a gas
by the addition of 8,180 calories. What is the heat of vaporization for this compound, in calories
per gram?
A. 25.0 cal/g
B. 327 cal/g
C. 540 cal/g
D. 8,140 cal/g
Answer: B
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10 Pro Tips to Easily Score 90+ in CBSE 10th, 12th Board Exam 2024 The Central Board of Secondary Education (CBSE) is set to conduct the Class 10 and 12 Board Exams in 2024, beginning on February 15 and concluding on April 02, 2024. Achieving a high score in the Class 10 board exam is important for students as it influences their choice of Arts, Commerce, or Science streams for Class 11. Likewise, Class 12 students need a good board exam score to secure admission to their preferred college and subject.
Getting a score of 90 or more in CBSE board exams may seem challenging, but it's possible with dedication and hard work. With the right approach and strategies, you can achieve this goal.
READ ALSO: CBSE Board Exam Class 12 Biology - Chapter-wise high scoring topics
Here are 10 expert tips to guide you in acing your exams in 2024:
Mastering NCERT: These textbooks lay the foundation for all CBSE questions. Before exploring other materials, grasp every concept, diagram, and example in the NCERT books.
Create a Study Plan that Fits You: One study plan doesn't work for everyone. Evaluate your strengths, weaknesses, and preferred learning methods to craft a practical schedule. Allocate specific time for each subject, including breaks, and make room for revision.
Engage in Active Learning: Simply reading won't do the work. Create mind maps, summarize key points, and practice problem-solving actively. This practice will help you solidify your understanding.
Don't Hesitate to Ask Questions: If you have doubts, ask! Approach teachers, classmates, or online forums to clear any confusion. Addressing questions early on prevents last-minute cramming.
Practice Regularly: Sample papers, past question papers, and mock tests are your allies. Regular practice familiarizes you with the exam pattern, question formats, and time management.
Prioritize Sleep and Health: While burning the midnight oil may seem appealing, prioritize quality sleep for optimal brain function and memory consolidation. Eat well, exercise regularly, and take breaks to avoid burnout.
READ ALSO: CBSE Class 10 Math Board exam is on March 11th 2024 - Top tips to score 100
Hone Your Writing Skills: Clarity, conciseness, and grammatical correctness matter for scoring well. Practice writing answers, especially for long-answer questions, under timed conditions.
Conquer Exam Day Anxiety: Stay calm on exam day. Deep breathing exercises can help manage anxiety. Bring all necessary stationery, arrive early, and carefully read instructions before attempting any question.
Believe in Yourself: A positive attitude is crucial! Trust your preparation, visualize success, and believe in yourself. You've got this!
Embrace Technology: Supplement your studies with educational apps, online tutorials, and interactive platforms. Technology can enhance learning, making it more engaging and effective.
Mon, 25 Dec 2023 21:23:00 -0600 en text/html https://timesofindia.indiatimes.com/education/news/10-pro-tips-to-easily-score-90-in-cbse-10th-12th-board-exam-2024/articleshow/106293809.cms
An Exploratory Look At Whether Generative AI Can Pass An Official Mental Health Counseling Licensing Exam That Professionals Take

In today’s column, I will be closely looking at whether generative AI could potentially pass an official mental health counseling licensing exam. This is part of my ongoing in-depth series about generative AI or large language models (LLMs) that are or can be anticipated to be used for mental health guidance or advisement.

Before I dive into today’s particular topic, I’d like to provide a quick background for you so that you’ll have a suitable context about the arising use of generative AI for mental health advisement purposes. I’ve mentioned this in prior columns and believe the contextual establishment is essential overall. If you are already familiar with the overarching background on this topic, you are welcome to skip down below to the next section of this discussion.

The use of generative AI for mental health treatment is a burgeoning area of tremendously significant societal ramifications. We are witnessing the adoption of generative AI for providing mental health advice on a widescale basis, yet little is known about whether this is beneficial to humankind or perhaps contrastingly destructively adverse for humanity.

Some would affirmatively assert that we are democratizing mental health treatment via the impending rush of low-cost always-available AI-based mental health apps. Others sharply decry that we are subjecting ourselves to a global wanton experiment in which we are the guinea pigs. Will these generative AI mental health apps steer people in ways that harm their mental health? Will people delude themselves into believing they are getting sound mental health advice, ergo foregoing treatment by human mental therapists, and become egregiously dependent on AI that at times has no demonstrative mental health improvement outcomes?

Hard questions are aplenty and not being given their due airing.

Furthermore, be forewarned that it is shockingly all too easy nowadays to craft a generative AI mental health app, and just about anyone anywhere can do so, including while sitting at home in their pajamas and not knowing any bona fide substance about what constitutes suitable mental health therapy. Via the use of what are referred to as establishing prompts, it is easy-peasy to make a generative AI app that purportedly gives mental health advice. No coding is required, and no software development skills are needed.

We sadly are faced with a free-for-all that bodes for bad tidings, mark my words.

I’ve been hammering away at this topic and hope to raise awareness about where we are and where things are going when it comes to the advent of generative AI mental health advisement uses. If you’d like to get up-to-speed on my prior coverage of generative AI across a wide swath of the mental health sphere, you might consider for example these cogent analyses:

  • (1) Use of generative AI to perform mental health advisement, see the link here.
  • (2) Role-playing with generative AI and the mental health ramifications, see the link here.
  • (3) Generative AI is both a cure and a curse when it comes to the loneliness epidemic, see the link here.
  • (4) Mental health therapies struggle with the Dodo verdict for which generative AI might help, see the link here.
  • (5) Mental health apps are predicted to embrace multi-modal, e-wearables, and a slew of new AI advances, see the link here.
  • (6) AI for mental health got its start via ELIZA and PARRY, here’s how it compares to generative AI, see the link here.
  • (7) The latest online trend entails using generative AI as a rage-room catalyst, see the link here.
  • (8) Watching out for when generative AI is a mental manipulator of humans, see the link here.
  • (9) FTC aiming to crack down on outlandish claims regarding what AI can and cannot do, see the link here.
  • (10) Important AI lessons learned from the mental health eating-disorders chatbot Tessa that went awry and had to be shut down, see the link here.
  • (11) Generative AI that is devised to express humility might be a misguided approach including when used for mental health advisement, see the link here.
  • (12) Creatively judging those AI-powered mental health chatbots via the use of AI levels of autonomy, see the link here.
  • (13) Considering whether generative AI should be bold and brazen or meek and mild when proffering AI mental health advisement to humans, see the link here.
  • (14) Theory of Mind (ToM) is an important tool for mental health therapists and the question arises whether generative AI can do the same, see the link here.
  • And so on.

Here’s how I will approach today’s discussion.

First, I will introduce you to a pioneering research study that sought to assess whether generative AI could potentially pass an exam taken by medical school students as part of their pursuit of achieving their medical degree. The exam is known as the United States Medical Licensing Exam (USMLE). This study received a great deal of headlines since it showcased that generative AI seems to do well on the arduous medical exams taken by budding doctors. Next, I will share with you some salient details about an exam for mental health professionals known as the National Clinical Mental Health Counseling Examination (NCMHCE).

I’m guessing you might be wondering whether generative AI might be able to do well on that type of exam. Great question, thanks. I opted to use a popular generative AI app called ChatGPT to try out a half-dozen questions from the NCMHCE. Please note that this was merely an official sample set and not by any means the full exam.

Would you be surprised to know that the generative AI was able to successfully answer many of the sampled sample questions? I provide some important caveats and limitations about this mini experiment of sorts, and I want to emphasize this was principally done on an ad hoc basis and merely intended to be illustrative.

Here’s the deal.

Please do not jump the shark on this matter. Hold your horses. My mainstay aims here are simply to inspire others to do a deep dive on this and perform a fully comprehensive rigorous research study of an akin nature, perhaps modeled somewhat on the same approach taken by the study on the USMLE or similar such professional licensing domains.

Anyway, I believe you will find this interesting, engaging, and possibly whet your appetite to find out more on these topics. My discussion is yet another angle to considering where we are and where things are going pertaining to generative AI and the field of mental health therapy.

Please buckle up and prepare yourself for quite a ride.

Generative AI And Medical School Standardized Licensing Exam

Let’s talk about tests.

We generally assume that to practice medicine a test of some kind should be required to attest to the proficiency of the person that will be serving as a medical professional. I’d like to start by discussing perhaps one of the most famous such medical proficiency tests known as the United States Medical Licensing Examination (USMLE). This is the test typically expected of those attaining a medical degree in the United States.

The USMLE was devised to aid in standardizing upon one major medical examination test that would be acceptable across every state and ensure that MDs were meeting the same set of standards. The test is composed of three separate stages and is taken during medical school and also upon graduation from medical school.

Here’s some additional detail as noted on the USMLE website:

  • “In the United States and its territories, the individual medical licensing authorities (‘state medical boards’) of the various jurisdictions grant a license to practice medicine. Each medical licensing authority sets its own rules and regulations and requires passing an examination that demonstrates qualification for licensure. Results of the USMLE are reported to these authorities for use in granting the initial license to practice medicine. The USMLE provides them with a common evaluation system for applicants for initial medical licensure.”
  • “USMLE was created in response to the need for one path to medical licensure for allopathic physicians in the United States. Before USMLE, multiple examinations (the NBME Parts examination and the Federation Licensing Examination [FLEX]) offered paths to medical licensure. It was desirable to create one examination system accepted in every state, to ensure that all licensed MDs had passed the same assessment standards – no matter in which school or which country they had trained.”
  • “The United States Medical Licensing Examination® (USMLE®) is a three-step examination for medical licensure in the U.S. The USMLE assesses a physician's ability to apply knowledge, concepts, and principles, and to demonstrate fundamental patient-centered skills, that are important in health and disease and that constitute the basis of safe and effective patient care.”

Humans take the USMLE to showcase their proficiency in medicine. When you encounter a medical doctor, you are likely to assume they probably took the test and passed it. On an intuitive basis we realize that having to pass such an arduous test is impressive and helps to provide us comfort that the person knows their stuff when it comes to the medical field.

Shift gears.

Can generative AI potentially also be proficient enough to pass the USMLE?

That’s an interesting and some would say important question worthy of considering.

First, some quick background about generative AI.

Realize that generative AI is not sentient and only consists of mathematical and computational pattern matching. The way that generative AI works is that a great deal of data is initially fed into a pattern-matching algorithm that tries to identify patterns in the words that humans use. Most of the modern-day generative AI apps were data trained by scanning data such as text essays and narratives that were found on the Internet. Doing this was a means of getting the pattern-matching to statistically figure out which words we use and when we tend to use those words. Generative AI is built upon the use of a large language model (LLM), which entails a large-scale data structure to hold the pattern-matching facets and the use of a vast amount of data to undertake the setup data training.

There are numerous generative AI apps available nowadays, including GPT-4, Bard, Gemini, Claude, ChatGPT, etc. The one that is seemingly the most popular would be ChatGPT by AI maker OpenAI. In November 2022, OpenAI’s ChatGPT was made available to the public at large and the response was astounding in terms of how people rushed to make use of the newly released AI app. There are an estimated one hundred million active weekly users at this time.

Using generative AI is relatively simple.

You log into a generative AI app and enter questions or comments as prompts. The generative AI app takes your prompting and uses the already devised pattern matching based on the original data training to try and respond to your prompts. You can interact or carry on a dialogue that appears to be nearly fluent. The nature of the prompts that you use can be a make-or-break when it comes to getting something worthwhile out of using generative AI and I’ve discussed at length the use of state-of-the-art prompt engineering techniques to best leverage generative AI, see the link here.

Shortly after ChatGPT was made publicly available, many AI researchers began to test the AI app by administering various well-known standardized tests to see how the AI app would do. In February 2023, a research study was posted that indicated ChatGPT had performed surprisingly well on the USMLE. The study was entitled “Performance of ChatGPT on USMLE: Potential for AI-Assisted Medical Education Using Large Language Models” by Tiffany H. Kung, Morgan Cheatham, ChatGPT, Arielle Medenilla, Czarina Sillos, Lorie De Leon, Camille Elepaño, Maria Madriaga, Rimel Aggabao, Giezel Diaz-Candido, James Maningo, Victor Tseng, PLOS Digital Health, and posted on February 9, 2023.

Here is what the research paper stated overall (excerpts):

  • “We evaluated the performance of a large language model called ChatGPT on the United States Medical Licensing Exam (USMLE), which consists of three exams: Step 1, Step 2CK, and Step 3. ChatGPT performed at or near the passing threshold for all three exams without any specialized training or reinforcement. Additionally, ChatGPT demonstrated a high level of concordance and insight in its explanations.”
  • “USMLE questions are textually and conceptually dense; text vignettes contain multimodal clinical data (i.e., history, physical examination, laboratory values, and study results) often used to generate ambiguous scenarios with closely-related differential diagnoses.”

Consider mindfully those above-noted remarks from the AI research effort.

ChatGPT was able to score either at or near the passing threshold for the three staged USMLE. Thus, an arduous medical proficiency exam that we expect human medical doctors to pass was nearly passed by a generative AI app. Some would decry this result as misleading in the sense that the generative AI was doing this without actual “knowledge” akin to what humans seem to possess. The concern is that generative AI is nothing more than a so-called stochastic parrot that mimics human wording and fails to “understand” or “comprehend” what is going on.

Nonetheless, the aspect that generative AI could accomplish such a feat is unto itself impressive, even if done via smoke and mirrors as some suggest. The result is additionally surprising because the researchers used ChatGPT out of the box, as it were, namely the generic version of ChatGPT. Another approach would be to add additional data training on the medical field to ChatGPT, but that’s not what they did in this experiment. A generic data-trained generative AI was able to do well on a highly specialized medical domain exam. For more about how generic generative AI can be fine-tuned to specific domains, see my coverage at the link here.

Let’s consider a few other detailed aspects about the notable research result and then I’ll move to my next topic of discussion.

The research paper noted these salient details (excerpted):

  • “The data analyzed in this study were obtained from USMLE sample question sets which are publicly available.”
  • “376 publicly-available test questions were obtained from the June 2022 sample exam release on the official USMLE website. Random spot checking was performed to ensure that none of the answers, explanations, or related content were indexed on Google prior to January 1, 2022, representing the last date accessible to the ChatGPT training dataset. All sample test questions were screened, and questions containing visual assets such as clinical images, medical photography, and graphs were removed. After filtering, 305 USMLE items (Step 1: 93, Step 2CK: 99, Step 3: 113) were advanced to encoding.”
  • “In this present study, ChatGPT performed at >50% accuracy across all examinations, exceeding 60% in most analyses. The USMLE pass threshold, while varying by year, is approximately 60%.”
  • “Therefore, ChatGPT is now comfortably within the passing range. Being the first experiment to reach this benchmark, we believe this is a surprising and impressive result. Moreover, we provided no prompting or training to the AI, minimized grounding bias by expunging the AI session before inputting each question variant, and avoided chain-of-thought biasing by requesting forced justification only as the final input.”

I’d like to bring your attention to a few points made in those excerpts.

Notice that the experiment consisted of identifying a sample of publicly available questions associated with the exam. The idea is to usually feed samples of questions and not necessarily an entire test per se. It is important to consider how a sample was chosen and whether the sample is suitably representative of what the full test might contain. Fair is fair.

Another fairness consideration is that there is always a chance that the generative AI might have been initially data-trained on the very same questions. If those questions were found when the startup data training took place, you could say it is absurd to feed the same questions into the generative AI. The answers will likely already be known simply due to having seen the questions and their answers beforehand.

If you select questions that arose after the cutoff date of the generative AI app’s data training, you are somewhat comfortable that the content wasn’t encountered already. But even that is readily questioned since the questions might have appeared in other guises. Some exams modify old questions and reuse them in later versions of the exam. There is a chance that a new question is close enough to an older question that perhaps this gives the generative AI a leg up on answering the new question.

My point is that you need to carefully consider how these experiments are conducted. Overall, make sure to look at what sample was chosen and how appropriate it is. What are the odds that the generative AI has previously encountered the same or similar questions? As much as feasible, the goal is to set a fair and square playing field to see whether the generative AI can genuinely answer questions that have not previously been used as part of the data training effort.

You now have a semblance of what takes place when trying to assess generative AI about being able to pass exams such as the pervasive USMLE in the medical domain.

Let’s continue our exploration.

Generative AI And Mental Health Therapy Exam Taking

The research study that explored the use of generative AI such as ChatGPT on the USMLE can serve as a role model for similar kinds of studies. The conception is to identify publicly available sample questions, administer the questions to the generative AI, and see how well or poorly the generative AI scores on answering the questions. As much as possible, try to keep the playing field level and fair.

I decided to try this quickly for the field of mental health therapy or mental health counseling.

There is a well-known exam known as the National Clinical Mental Health Counseling Examination (NCMHCE). Sample questions are publicly posted online. I selected some of the sample questions and fed them into ChatGPT. I opted to use ChatGPT due to its immense popularity and it has generally been the default choice of similar research studies.

I might note that a more advanced generative AI such as GPT-4 by OpenAI or others would likely do a better job than ChatGPT. In that manner, you could interpret the ChatGPT usage as the floor and that we might expect heightened results by using a more advanced generative AI app. There isn’t an ironclad guarantee that a more advanced generative AI will do better. The odds though are in that direction.

We also have to be watchful for in a sense polluting an experiment by perchance using questions that have already been seen by the generative AI during the initial data-training. Furthermore, if the generative AI is hooked up to the Internet, the AI might simply go out and find the questions and their answers, similar to a search engine, rather than trying to directly answer the questions. ChatGPT in that sense is a handy choice because the free version does not readily allow for Internet access to perform its activities and the data training was last cut off in January 2022 (at the time of writing of this discussion).

Let’s dive into the ad hoc experiment by first establishing the nature of the mental health therapy or mental health counseling exam.

The National Clinical Mental Health Counseling Examination (NCMHCE) is devised and administered via an organization known as the National Board for Certified Counselors, Inc. Here is what the website for the organization says (excerpts):

  • “The National Board for Certified Counselors, Inc. and Affiliates (NBCC) is the premier credentialing body for counselors, ensuring that counselors who become nationally certified have achieved the highest standard of practice through education, examination, supervision, experience, and ethical guidelines.”
  • “Established as a not-for-profit, independent certification organization in 1982, NBCC’s original and primary purposes have broadened, and its divisions and affiliates have taken on additional responsibilities to advance the counseling profession and enhance mental health worldwide.”
  • “Today, there are over 69,000 National Certified Counselors (NCCs) in more than 40 countries.”

The gist is that this is a well-known and widely accepted organization, and the exam is likewise well-known and widely accepted. I bring this up in case you read a study that used generative AI on some relatively unknown exam or less than a stellar reputational exam, in which case, you would want to gauge the result of the study as partially on the rigor and standing of the test being given at the get-go.

Here is what the website about the NCMHCE says about the exam (excerpts):

  • “The National Clinical Mental Health Counseling Examination (NCMHCE) is designed to assess the knowledge, skills, and abilities determined to be important for providing effective counseling services. The NCMHCE is a requirement for counselor licensure in many states. It is one of two examination options for the National Certified Counselor (NCC) certification and also fulfills the examination requirement for the Certified Clinical Mental Health Counselor (CCMHC) specialty certification.”
  • “The NCMHCE measures an individual’s ability to apply and evaluate knowledge in core counselor skills and competencies and to practice competently as a professional counselor. Specifically, it assesses an entry-level clinical mental health counselor’s ability to apply knowledge of theoretical and skill-based tenets to clinical case studies. The case studies are designed to capture a candidate’s ability to identify, analyze, diagnose, and develop plans for treatment of clinical concerns.”
  • “Candidates for the NCMHCE must have a graduate-level degree or higher from a counseling program accredited by the Council for Accreditation of Counseling and Related Educational Programs (CACREP) or administered by an institutionally accredited college or university. The counseling degree program must contain courses in eight requirement areas.”

Observe some key points mentioned in those excerpts.

First, the exam is used to assess entry-level clinical mental health counselors. You might say that this is handy for my ad hoc experiment since I want to focus on the keystone threshold needed to be considered suitably knowledgeable for proceeding to perform mental health therapy with actual clients or patients. Other exams might be used to assess more advanced skill levels, but I’m aiming here to start with the usual starting point. I’m sure that other researchers are or will try to do the same for more advanced instances.

Second, note that candidates who want to sit for the exam must have a graduate-level degree or higher from an accredited counseling program or as administered by an accredited college or university. This sets the bar higher than perhaps allowing an undergraduate to take the exam or maybe wantonly opening the exam to anyone who wants to take it. We can presume that the test is likely to ask questions of a hard nature. That’s good since we would want to make sure we give something challenging to generative AI rather than some easy-peasy questions or materials. We might also note that of course, generative AI would not qualify to officially take the exam since it has not met all the criteria to do so.

The official exam website provides an NCMHCE Sample Case Study that indicates the case study is considered updated as of March 2023. I selected six sample questions from this sample set. I want to loudly emphasize that this is an ad hoc selection and I do so merely to be illustrative of what might be done on a more rigorous basis.

Though the date says March 2023, there of course is a chance that these questions and their answers have been around before that date, for which ChatGPT might have seen before the January 2022 cutoff date. I tried to do various probing into ChatGPT to see if the content had already been prior encountered. By and large, it doesn’t seem to be, but that’s not known for sure, and a deeper analysis would need to be undertaken to ascertain this. For the moment, let’s go with the flow and assume that the sample questions weren’t previously seen by ChatGPT during its data training.

The six sampled sample questions cover these six respective topics:

  • Q1. Establish a therapeutic alliance.
  • Q2. Identify strengths that improve the likelihood of goal attainment.
  • Q3. Discuss limits of confidentiality.
  • Q4. Determine a diagnosis.
  • Q5. Assess the presenting problem and level of distress.
  • Q6. Establish short- and long-term counseling goals consistent with the client’s diagnosis.

Keep that in mind as I walk you through what ChatGPT provided as answers to the posed questions.

The test is essentially based on case studies. For these six sampled sample questions, a case study was provided in the publicly posted material. The case study was fed into ChatGPT for this analysis. Rather than displaying for you the entirety of the case study, I will do a quick recap to bring you up to speed.

In this instance, the case study entails a divorced female of age 35 who is first undertaking a mental health counseling session with a mental health therapist who has some background about the client or patient but otherwise, this is the first meeting of the two. The client or patient has already been provisionally diagnosed as having a major depressive disorder.

Additional background is given about the client or patient. For example, after her divorce, she began staying in bed quite a lot and moved back in with her mother. She got fired from her job. She has had financial difficulties. Most days, she feels sad, empty, and anxious. She has joint legal custody with her ex-husband of their two children, respectively aged 10 and 12. And so on.

That outlines the nature of the underlying case study.

Questions And The Answers Generated By The Generative AI

I am going to walk you through each of the six multiple-choice questions and also showcase the answers that were generated by ChatGPT so that you can follow along step-by-step.

My initiating prompt asks ChatGPT to provide answers plus explain why each chosen answer was considered the correct answer by ChatGPT. Asking for an explanation is not necessary, but I thought getting explanations might be interesting to see.

There is also a bit of prompting strategy involved, namely that by asking for an explanation the chances are that a generative AI app might be more extensive in trying to solve a given question or problem, see my discussion at the link here. You could suggest that I was aiding the generative AI by giving an establishing prompt that would urge it to do a better job than otherwise. Whenever you look at research studies doing just about anything with generative AI, make sure to find out what prompts they used. This is a significant factor related to the performance of the generative AI. Studies that fail to showcase their prompts are unfortunately doing a bit of a disservice by not revealing how they got the generative AI to undertake things.

The sampled sample questions are based on the case study, and I’ve placed them in quotes to indicate they came from the case study. In some instances, the wording is slightly reworded merely and strictly for purposes of feeding them into ChatGPT.

I am purposely not going to speculate or comment on the answers that are given by ChatGPT. I will simply note whether ChatGPT selected the correct multiple-choice selection as stipulated in the sample set. I’m guessing that mental health therapists and mental health counselors will undoubtedly find the explanations of special interest and will indubitably get them extensively mulling over what ChatGPT had to say.

Here is my establishing prompt and the answer provided by ChatGPT:

  • My prompt: “I am going to ask you a series of questions about the case study. The questions are multiple-choice. You are to select an answer of either A, B, C, or D from the choices provided for each question. I want you to carefully read each question and then indicate which answer is the best answer of the choices provided. You are to also explain why the answer you’ve chosen is the best answer. Are you ready to answer the first question?”
  • ChatGPT generated a response: “Yes, I'm ready. Please proceed with the first question.”

Okay, we are good to go.

Here’s the first question:

“Q1. What would be the best way for a mental therapist to at initial intake seek to establish a therapeutic relationship with this person?”

“A. Reflect on what is irrational about the woman’s self-evaluations.”

“B. Advise her about how to manage her painful experiences.”

“C. Summarize her emotions and her desire for change.”

“D. Explore how she could improve her role in relationships.”

  • ChatGPT generated response:

“Answer to Q1 is C. Summarize her emotions and her desire for change.”

“Explanation: Establishing a therapeutic relationship involves demonstrating empathy, understanding, and acknowledging the client's emotions. Summarizing her emotions and desire for change (Option C) helps validate her experiences and shows that the therapist is actively listening and comprehending her situation. This approach helps build rapport and sets the foundation for a collaborative therapeutic relationship. It also allows the client to feel heard and understood, creating a safe space for further exploration and intervention.”

End of ChatGPT response

ChatGPT selected answer C, which is considered the correct answer as per the sample set.

The explanation indicates the presumed basis. I could have also asked ChatGPT to explain why the other answers weren’t as good as the selected answer. That would have gotten quite wordy and didn’t seem needed for this ad hoc analysis. A deeper study might want to consider getting a lengthier reply with a more elaborate explanation.

We move to the second question:

“Q2. What information should a mental health therapist explore to help the client remember her strengths?”

“A. The client’s disclosure about her previous level of functioning.”

“B. The client’s recollections of past mistakes.

“C. The client’s feelings about her mother paying for counseling.”

“D. The client’s use of cognitive distortions.”

  • ChatGPT generated response:

“Answer to Q2 is A. The client’s disclosure about her previous level of functioning.”

“Exploring the client's disclosure about her previous level of functioning (Option A) would be most relevant to identify and highlight her strengths. Understanding her past achievements, abilities, and positive aspects of her life before the onset of depressive symptoms can help the therapist and the client focus on resources that can be tapped into for recovery. This approach emphasizes a strengths-based perspective and encourages the client to recognize her capabilities, fostering a more positive outlook on her current situation.”

End of ChatGPT response

The answer indicated by ChatGPT is answer A, which is also the correct answer per the sample set. We are now two-for-two in terms of having gotten the questions right.

Next, let’s see what happens with the third question:

“Q3. How should a mental health therapist handle the client’s concern about confidentiality?”

“A. Explain that as a mental therapist, they will share progress updates with the mother but not provide any details.”

“B. Clarify what can and cannot be shared through informed consent.”

“C. Request the mother’s assent to the treatment and obtain the client’s consent.”

“D. Ask the client for a signed release that would include her mother as part of the treatment process.”

  • ChatGPT generated response:

“Answer to Q3 is B. Clarify what can and cannot be shared through informed consent.”

“The best approach is to clarify confidentiality issues through informed consent (Option B). The therapist should explain the limits of confidentiality, outlining what information can be shared and under what circumstances. This empowers the client to make an informed decision about what aspects of her treatment will remain confidential and what may need to be shared. It respects the client's autonomy and privacy while addressing concerns about the involvement of her mother in the therapy process.”

End of ChatGPT response

The answer indicated by ChatGPT is B, which is also the correct answer via the sample set. Three for three, so far.

Here’s the fourth question:

“Q4. From the perspective of the mental health therapist, what self-report from the client is a discernable criterion for the given diagnosis?”

“A. Her disagreements with her mother.”

“B. Her increased psychomotor agitation.”

“C. Her difficulty managing her children.”

“D. Her decreased pleasure in her activities.”

  • ChatGPT generated response:

“Answer to Q4 is D. Her decreased pleasure in her activities.”

“A discernible criterion for a major depressive disorder is often a pervasive and persistent decrease in pleasure or interest in previously enjoyed activities. This is commonly referred to as anhedonia. The client's report of not being able to remember the last time she enjoyed something and the cessation of an activity she used to find enjoyable (creating jewelry) aligns with this criterion. While other aspects, such as disagreements with her mother (Option A) or difficulty managing her children (Option C), may be related to her emotional state, the decreased pleasure in activities is a more specific indicator for depression.”

End of ChatGPT response

The answer indicated by ChatGPT is D, which is the correct answer according to the sample set. You will also note that this time the explanation opted to explain why the chosen answer was considered a better choice than some of the other proposed answers.

We are at four of four in terms of getting the correct answers.

Let’s see what occurs with the fifth question:

“Q5. What criteria were likely used to determine the severity specified for the client’s major depressive disorder?”

“A. The number of symptoms is not substantially in excess of those needed to make the diagnosis, yet functional impairment exists.”

“B. The intensity of the symptoms is distressing but manageable, and results in minor impairment of social or occupational functioning.”

“C. The intensity of symptoms is high and is accompanied by psychomotor agitation and mixed features associated with a major depressive episode.”

“D. The number of symptoms and their intensity are distressing and unmanageable and interfere with the client’s social and occupational functioning.”

  • ChatGPT generated response:

“Answer to Q5 is D. The number of symptoms and their intensity are distressing and unmanageable and interfere with the client’s social and occupational functioning.”

“The severity specified for major depressive disorder is often determined by the number and intensity of symptoms, as well as their impact on the client's social and occupational functioning. In this case, the client reports feeling sad, empty, and anxious most days, experiences tension every day, has difficulty getting out of bed, and has ceased engaging in previously enjoyed activities. Additionally, she has lost her job, faces financial difficulties, and expresses guilt about her interactions with her children. These factors suggest a significant impact on both social and occupational functioning, aligning with the criteria for a more severe form of major depressive disorder.”

End of ChatGPT response

ChatGPT indicates the answer should be D, but the sample set indicates the correct answer is A. This is the first miss by ChatGPT in this set of sampled sample questions.

I was curious whether ChatGPT could justify the answer D versus the answer A.

One thing to keep in mind is that some generative AI apps such as ChatGPT tend to immediately back down when you challenge them about an answer. The AI will indicate that it was wrong and switch to the answer that you say is the correct answer. In a sense, this is somewhat disconcerting because you would generally prefer the AI to moderately push back and support an answer that was chosen.

I am not saying this should be doggedly dogmatic. It certainly makes sense to have the AI back down if the user is telling the AI that the answer is wrong, though I think you can see how this might be a dangerous slope to slide upon. Just because a user says that something is wrong does not mean that it indeed is wrong. You can end up with the AI being too easily convinced of falsehoods simply because the user said they were truths. This is an ongoing issue of concern about generative AI, see my analysis at the link here.

All in all, rather than telling the AI that it was wrong, which would possibly get it to collapse like a deck of cards, I came to the matter politely and tangentially. I merely asked ChatGPT to compare the answer D with the answer A. I didn’t tip my hand that the answer A is considered the correct answer.

Here we go:

  • My prompt: “I'd like you to compare the answer D that you selected to the choice A. What makes answer D better than answer A?”
  • ChatGPT generated response:

“Let's compare options D and A:”

“D. The number of symptoms and their intensity are distressing and unmanageable and interfere with the client’s social and occupational functioning.”

“A. The number of symptoms is not substantially in excess of those needed to make the diagnosis, yet functional impairment exists.”

“The main distinction lies in the severity of functional impairment. Option D suggests that the number of symptoms and their intensity are distressing and unmanageable, leading to significant interference with social and occupational functioning. This aligns with a more severe form of major depressive disorder, where the impairment is substantial.”

“On the other hand, Option A acknowledges functional impairment but does not emphasize the distressing and unmanageable nature of the symptoms. It leaves room for interpretation that the impairment might be present but not to the extent that it is distressing or unmanageable.”

“In the context of major depressive disorder, severity is often determined not just by the presence of symptoms but also by the impact on daily functioning. Option D explicitly addresses the distress and interference with functioning, making it a more comprehensive and accurate description of a severe presentation of major depressive disorder.”

End of ChatGPT response

I’d bet that mental health professionals could readily debate the indication of ChatGPT about why one of the answers is construed as better than the other. Please do so at your leisure.

Moving to the sixth and final question, take a look at this one:

“Q6. From the perspective of the mental health therapist, what short-term goal would guide the treatment of the client’s presenting problem?’

“A. Spend one hour each day rewriting difficult childhood memories with her mother.”

“B. Return to her previous sleep schedule to be more present with her children.”

“C. Spend one hour each day journaling about her vision for her next marriage.”

“D. Return to full-time work in nursing to boost her self-image and mood.”

  • ChatGPT generated response:

“Answer is B. Return to her previous sleep schedule to be more present with her children.”

“The most appropriate short-term goal would be to address the immediate issue affecting her daily functioning, which is her disrupted sleep schedule. Returning to her previous sleep schedule (Option B) can have a positive impact on her mood, energy levels, and ability to engage with her children. This goal is specific, achievable, and directly addresses a key factor contributing to her distress. It also aligns with improving her overall well-being and functioning in the short term.”

End of ChatGPT response

The answer selected by ChatGPT is answer B, which is the correct answer according to the sample set.

Conclusion

Please do not try to calculate the percentage of times that ChatGPT was able to correctly answer the sampled sample. Doing so would be ridiculous as this was not a rigorous experiment but instead an ad hoc one and meant solely to get the juices flowing about examining how generative AI might do on these kinds of tests. That being said, I believe there is enough indication that performing a thorough study would be worthwhile. Had the generative AI got none of the answers correctly I would probably be leaning toward suggesting that a full-on study wasn’t likely worth the effort. This snippet suggests that doing more might be valuable.

There are flies in the ointment on these matters.

One argument about having generative AI take these various types of tests is that there isn’t any human soul or human connection that is also being assessed, see my discussion at the link here. In other words, just because a generative AI app can answer questions on a dry test does not mean that we would want the generative AI to interact with real people in real life and provide advice as to whatever domain or realm of expertise is being tested.

The argument is further amplified when considering the topic of mental health. Some would assert that only another human can adequately counsel another human. An AI system is not human and does not have human experience under its belt. A counterviewpoint is that notwithstanding humanness, there is still a place for AI to aid humans, including in the sphere of mental health guidance or advice.

Let’s conclude this discussion for now by invoking a famous line.

The renowned American psychologist Carl Rogers purportedly said this: “In my early professional years, I was asking the question, how can I treat, or cure, or change this person? Now I would phrase the question in this way, how can I provide a relationship that this person may use for their personal growth?”

Can generative AI form a relationship with humans and if so, do we want that to be how mental health is conveyed or advised?

More questions ostensibly need more answers; thus, the endeavor must continue.

Mon, 01 Jan 2024 09:36:00 -0600 Lance Eliot en text/html https://www.forbes.com/sites/lanceeliot/2024/01/01/an-exploratory-look-at-whether-generative-ai-can-pass-an-official-mental-health-counseling-licensing-exam-that-professionals-take/
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Graduates of this program are able to provide safe, compassionate, culturally competent anesthesia care in rapidly changing practice environments across the lifespan. The DNP in Nurse Anesthesia educational experience will facilitate your professional growth and development into a competent, knowledgeable and vigilant nurse anesthesia practitioner.

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References will not be accepted from colleagues, friends, CRNA/anesthesiologist shadowed by the applicant, or family members

Applicants may use Drexel's electronic letter of recommendation service.

If a recommender prefers to submit an original, hard copy letter, please remind them that it must include an ink signature and be submitted in a sealed envelope.

Clinical Critical Care Experience:
The Council on Accreditation of Nurse Anesthesia Programs defines a critical care setting:
Critical care experience must be obtained in a critical care area within the United States, its territories or a US military hospital outside of the United States. During this experience, the registered professional nurse has developed critical decision-making and psychomotor skills, competency in patient assessment and the ability to use and interpret advanced monitoring techniques. A critical care area is defined as one where, on a routine basis, the registered professional nurse manages one or more of the following: invasive hemodynamic monitors (e.g., pulmonary artery, central venous pressure and arterial catheters), cardiac assist devices, mechanical ventilation and vasoactive infusions. Examples of critical care units may include but are not limited to: surgical intensive care, cardiothoracic intensive care, coronary intensive care, medical intensive care, pediatric intensive care and neonatal intensive care. Those who have experiences in other areas may be considered provided they can demonstrate competence with managing unstable patients, invasive monitoring, ventilators and critical care pharmacology.

  • Applicants to the DNP-NA program will possess a minimum of two years (post-orientation) full-time experience working with the most critically ill patients.

Technical Standards:
The Nurse Anesthesia Program requires that all applicants must be able to meet the technical standards for admission, progression and graduation. The technical standards can be found by clicking on this link: Technical Standards-Nurse Anesthesia.

Tuition and Fees:
Cost of Attendance for Graduate Programs

YEAR 1
DNP Program and Project Introduction
Advanced Pharmacology
Introduction to Advanced Practice Nursing: Professional and Self Development
Intro to Biostats
Advanced Physiology
Evidence-Based Practice: Processes, Structures and Impact
Health Policy and Politics
Applied Epidemiology
Introduction to Nurse Anesthesia Practice
Advanced Health Assessment & Diagnostic Reasoning
Appraising Quantitative Evidence: Research Designs and Methodologies
Clinical Ethics and Health Law
Fundamental Principles of Nurse Anesthesia Practice
Overview of Nurse Anesthesia
Introduction to Simulation for Nurse Anesthesia Practice
Appraising Qualitative Evidence: Research Designs and Methodologies
YEAR 2
Nurse Anesthesia Pharmacology I
Nurse Anesthesia Practice Principles I
Clinical Practicum I
Advanced Pathophysiology
Leadership and Management in Healthcare Delivery Systems
Nurse Anesthesia Pharmacology II
Nurse Anesthesia Practice for Special Populations I
Intraprofessional Communication Simulation
Clinical Practicum II
Advanced Pathophysiology II
Nurse Anesthesia Practice for Special Populations II
Nurse Anesthesia Practice Principles II
Clinical Practicum III
Advanced Physiology III
Health Information Technology and Information Systems
Nurse Anesthesia Practice for Special Populations III
DNP Project I: Practice Problem and Search for Evidence
Clinical Practicum IV
YEAR 3
Nurse Anesthesia Practice for Special Patient Populations IV
DNP Project II: Project Planning and Design
Clinical Practicum V
Nurse Anesthesia Practice for Special Patient Populations V
Crisis Management in Anesthesia Practice
Clinical Practicum VI
Anesthesia Practice Correlative Seminar I
Nurse Anesthesia Professional Development
DNP Project III: Project Implementation
Clinical Practicum VII
Nurse Anesthesia Practice Correlative Seminar II
DNP Project IV: Project Evaluation and Dissemination
Clinical Residency I
YEAR 4
Anesthesia Practice Curricular Review and Certification Exam Preparation
Clinical Residency II

 

Drexel University College of Nursing and Health Professions Master of Science in Nurse Anesthesia (MSN) program and Doctor of Nursing Practice program received a maximum 10-year continuous accreditation in 2021 from the Council on Accreditation of Nurse Anesthesia Educational Programs (COA). The next scheduled review will be in 2031.

Graduates of accredited programs are eligible to take the National Certification Exam (NCE) administered by the National Board of Certification and Recertification for Nurse Anesthetists (NBCRNA) to be certified in every state as a Certified Registered Nurse Anesthetist (CRNA). 

Council on Accreditation of Nurse Anesthesia Educational Programs (COA)
10275 W. Higgins Rd., Suite 906
Rosemont, IL 60018-5603
Main Number: 224-275-9130
Fax: 847-728-8128
Email: accreditation@coacrna.org

The Doctor of Nursing Practice program at Drexel University is accredited by the Commission on Collegiate Nursing Education, 655 K Street NW, Suite 750, Washington, DC 20001, 202-887-6791.

Program Outcomes

  • Evaluate evidence-based processes that address practice standards to promote safe and equitable patient-centered care
  • Implement pertinent evidence-based findings to foster practice guidelines that improve population health and the practice environment to support safe and equitable patient-centered care
  • Integrate technology and information systems with advanced nursing practice to improve patient care
  • Demonstrate expertise in the analysis, formulation and implementation process of health care policy
  • Contribute to the advancement of anesthesia practice, health care and humanity through leadership, education and promotion of intraprofessional and interprofessional collaboration
  • Utilize advanced critical thinking and decision-making skills throughout the perioperative period, during the autonomous administration of anesthesia for patients across the lifespan
  • Demonstrate advanced levels of nursing practice, clinical judgment and accountability to deliver optimal patient care with an emphasis on diverse and vulnerable populations
  • Attainment of Council on Accreditation requirements as specified by the NBCRNA that prepares the graduate for certification eligibility

Upon successful completion of the DNP-NA program's outcomes, as specified by the Council on Accreditation for Nurse Anesthesia Educational Programs (COA), the student is awarded a DNP and is eligible to take the National Certification Exam (NCE) offered by the National Board of Certification and Recertification for Nurse Anesthetists (NBCRNA).

Drexel University Nurse Anesthesia MSN class of 2023 had an 88% first-time taker pass rate, and a 100% pass rate for repeat takers of the NBCRNA's NCE. The average number of clinical cases for the MSN class of 2023 was 850 with an average number of clinical hours of 2,600. The employment rate within six months of graduation for all of Drexel's 2023 MSN graduates was 100%.

Nursing Program Mission and Values

The Nursing program faculty and staff are committed to educating nurses to embrace both the art and science of nursing, to integrate caring into practice, to think critically and practice competently, compassionately and safely in complex rapidly changing practice environments. The Nursing program supports administrators, faculty, staff and students in developing holistic evidence-based programs, which create healing and caring environments at all levels that reflect innovative education, interdisciplinary practice and research.

The highest goals of the program include demonstrating efficacy, quality and cost-effectiveness of evidence-based nursing interventions in promoting health, preventing disease, preserving human dignity, reducing health disparities and caring for the sick and injured.

All efforts in the Nursing program are designed to build nursing knowledge, enhance nursing practice, foster professional integrity, promote innovation, engage in interdisciplinary collaboration and ultimately improve the health outcomes of patients and families from diverse communities across the continuum of care.

The Nursing program is committed to excellence in nursing education through educational programs that are:

  • Authentic: We value a deep connection to others, appreciation of diverse opinions and respect for the other’s frame of reference. We value going beyond objective assessment to understand the context of the other, creative use of self and engagement in the artistry of the caring-healing process—the essence of Nursing.
  • Complex: We value complexity science by recognizing that our Nursing program is a complex, dynamic, unpredictable, emerging, self-organizing and adaptive system that cannot be reduced to the sum of its members. As such, we seek that administrators, faculty, staff and students develop skills to improvise, build on the innovations of others, develop positive interpersonal interactions, appreciate the reciprocity that our actions have on the larger system and ourselves and embrace surprise as an opportunity to learn, make sense of our dynamic reality and make a positive impact on the health of our clients.
  • Rigorous: We continuously review and redesign programs, courses, technology-infused learning systems and educational products for depth and quality with the learner's experience and background in mind.
  • Relevant: We continuously examine changing market forces, the progression of nursing knowledge and the best practices in health care and education to redesign educational programs. There is a strong commitment to population-focused care practice in diverse communities; therefore, we strive to ensure clinically relevant education and simulation experiences. Our faculty serves as excellent role models for developing clinicians.
  • State of the Art: We value the use of technological innovation and patient simulation scenarios which foster advanced and evidence-based interdisciplinary communication and teamwork, multiple patient management and crisis resource management skills, provision of culturally relevant care, decreased prevalence of errors and adverse events and a higher intellectual standard in both undergraduate and graduate nursing education.
  • Learner-Friendly: In consideration of the demanding, fast-paced lives of our students and nurse consumers, we offer high-quality educational programs in convenient and contemporary formats, including access to academic online courses with attention to user-friendly interfaces as well as resources to enhance academic success. In addition, the College of Nursing and Health Professions is committed to being a challenging and rewarding work environment for faculty, staff and administrators. Information and data on all aspects of the college’s operations are widely shared with faculty, staff and students and all are encouraged to participate in its decisions and activities.
Sat, 27 May 2023 08:00:00 -0500 en text/html https://drexel.edu/cnhp/academics/doctoral/dnp-na/
ISU students spearhead statewide mental health initiative

College students across Illinois might be able to take time off for mental wellness, and Illinois State University's student government is spearheading the initiative to make it happen.

Student Body president Eduardo Monk said the idea is to give every college student across the state a handful of days designated for mental health. Each student can opt to take a day off when they most need it.

“Mental health is unpredictable,” Monk said. “You don't get to pick which days your mental health is going to hold you back a little bit.”

The goal is to expand state law granting five mental health days to K through 12 students statewide so that it incorporates universities. That’s laid out in a bill introduced by state Sen. Dave Koehler, D-Peoria, in October, though ISU student government has been working on the initiative since last year.

Monk said he and others recognize there are still kinks to hammer out, as K-12 is “simpler than higher ed.” In college, most classes meet two or three times a week, and some only meet once per week. There’s also the issue of exams, and depending on the major, labs or practical evaluations.

“There's going to be a lot of extra exceptions and provisions that are going to be provided within this,” Monk said.

Pilot programs

Other schools are collaborating to determine what might work. Southern Illinois University at Edwardsville is planning a pilot program for its version of the mental health days.

SIUE and ISU are consulting Northeastern University in Boston that allows students two wellness days in the spring semester. Their program has been in place since 2022, though they advertise it as still being in the pilot phase.

Northeastern prohibits students from using wellness days on exam dates, called “blackout days.”

Isabella Pruitt, an executive board member for SIUE student government, said Edwardsville plans to use Northeastern’s framework. Ideally, students can go into their portal and request days off.

She said SIUE’s program likely won’t be operational until Fall 2024, but conversations are ongoing with the administration. Overall, Pruitt said people are “generally in favor, just a little skeptical.”

“I think everyone recognizes the importance of mental health and mental health for students, but I think the problem is that people don't want to put in the effort to actually address these issues,” she said.

Flexibility for mental health

Andy Morgan is the assistant vice president and dean of students at ISU. He also advises the Student Government Association [SGA].

Morgan pointed out that ISU has an abundance of mental health services for students, but not every student is aware of them, adding services do not equate to time off, and students go through Fall semester with only one break.

“Some of our students, they’re [putting] the pedal to the metal for those 16 weeks, and they burn themselves out,” he said.

Mental health days, Morgan said, also can encourage dialogue.

“How can we better educate our students to better communicate with their faculty?” Morgan said. “I think that's one thing, but also just faculty continuing to have that empathy and understanding when their students do need that time and to focus on their own mental well being is important.”

Excused absences are currently allowed at ISU only for the case of bereavement for the loss of a relative or in the event of military obligations — a state mandate. Professors are given discretion on any further absences, including for illness, hospitalization, or bereavement for the loss of a non-relative — all considered unexcused under university policy.

Individual colleges also can make their own guidance. For example, Emma Beddow, a student at the Wonsook Kim College of Fine Arts and a student government member, said they were given a school-wide wellness day in Fall. Classes were canceled that day, and the college provided activities surrounding wellness for further encouragement.

“That was one of the things that made me think, why aren't we just doing this anyway? Why aren't we advocating for wellness anyway?” Beddow said about the wellness day.

As someone who has anxiety, she added that the downside to a designated wellness day is that she can’t plan her stress. She said that’s why she’s hoping for the flexibility offered in the bill amendment.

Lake Land College Student Government president Madilyn Brummer said now is the time to get this legislation out there. Lake Land, in Mattoon, is a recent collaborator.

Brummer called mental health a “nonpartisan issue.” [Koehler, who filed the bill, is a Democrat.]

“I think that that is where our main drive is coming from to just speak out for the students and stand up for the students in our college,” she said.

Brummer said the concept also is beneficial to the universities, as it can be used to track a student’s mental health. How many days students take off can be an indicator.

ISU student government member Kerem Tasdan said he thinks the wellness days structure will better prepare students for life outside of college. When looking for a job, he said people are told to consider benefits like sick days and vacation time. Mental health shouldn’t be any different.

“Stuff like that is always in the conversation for our future, and it should be something we're thinking about even now as students you know, being able to choose when you need to take that time,” he said.

If the legislation isn’t enacted, Tasdan said he still thinks the students are having an impact.

“I hope at the very least it opens the eyes of people who may not have been as exposed to the issue,” he said.

Monk, ISU's Student Body president, said he is preparing to make any concessions to the initial asks.

“We're gonna be willing to compromise on everything with this,” he said. “We're trying to pass a state law here, so if they come to us and say, ‘Hey, we're giving you a one day a year,’ we're taking it happily.”

We depend on your support to keep telling stories like this one. WGLT’s mental health coverage is made possible in part by Report For America and Chestnut Health Systems. Please take a moment to donate now and add your financial support to fully fund this growing coverage area so we can continue to serve the community.

Mon, 18 Dec 2023 08:09:00 -0600 en text/html https://www.wglt.org/local-news/2023-12-18/isu-students-spearhead-statewide-mental-health-initiative
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