An interprofessional cohort analysis of student interest in medical ethics education: a survey-based quantitative study
Bibliographic record
Abstract
Abstract Background There is continued need for enhanced medical ethics education across the United States. In an effort to guide medical ethics education reform, we report the first interprofessional survey of a cohort of graduate medical, nursing and allied health professional students that examined perceived student need for more formalized medical ethics education and assessed preferences for teaching methods in a graduate level medical ethics curriculum. Methods In January 2018, following the successful implementation of a peer-led, grassroots medical ethics curriculum, student leaders under faculty guidance conducted a cross-sectional survey with 562 of 1357 responses received (41% overall response rate) among students enrolled in the School of Medicine, College of Nursing, Doctor of Physical Therapy and BS/(D) MD Professional Scholars programs at The Medical College of Georgia at Augusta University. An in person or web-based questionnaire was designed to measure perceived need for a more in-depth medical ethics curriculum. Results The majority of respondents were female (333, 59.3%), white (326, 58.0%) and mid-20s in age (340, 60.5%). Almost half of respondents (47%) reported no prior medical ethics exposure or training in their previous educational experience, while 60% of students across all degree programs reported an interest in more medical ethics education and 92% noted that an understanding of medical ethics was important to their future career. Over a quarter of students (28%) were interested in pursuing graduate-level training in medical ethics, with case-based discussions, small group peer settings and ethics guest lectures being the most desired teaching methods. Conclusions The future physician, nursing and physical therapist workforce in our medical community demonstrated an unmet need and strong interest for more formal medical ethics education within their current coursework. Grassroots student-driven curricular development and leadership in medical ethics can positively impact medical education. Subsequent integration of interprofessional training in medical ethics may serve as a vital curricular approach to improving the training of ethically competent healthcare professionals and overcoming the current hierarchical clinical silos.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".