The Health and Wellness of Future Physicians: Barriers to Change and Innovations in Undergraduate Medical Curricula
Bibliographic record
Abstract
Objectives: 1) To build on existing theory about the health and wellbeing of medical students and physicians; 2) To explore barriers to educational and institutional change; 3) To identify current educational innovations that improve the resilience of students and future doctors.Methods: Current explanatory models and research findings have been augmented through interviews with key academics and educators in North America, Australia and UK, as well as through reviewing innovative curricular interventions related to health and wellness of students and physicians.Results: Medical students are at risk of anxiety, depression and burnout,(1) while doctors suffer a range of occupational health hazards that illustrate psychological vulnerability.(2) Many commentators have called for changes to undergraduate education to improve the general resilience of doctors.(3, 4) The long delay in addressing physician stress may relate to the tacit assumptions of biomedicine that act as guiding principles in clinical practice.(5) While helpful in increasing the efficacy of modern medicine, these assumptions act as barriers to more progressive curricula. A ‘whole person care’ paradigm of patients is becoming well theorised, but acknowledging the ‘whole person’ of the student and physician is still work-in-progress. Fortunately, recent initiatives in many countries illustrate substantial progress. Undergraduate programs now focus on self-awareness, interpersonal skills, work-life balance, whole person care, and career choice. Methods vary widely and include coaching on physical, mental, and emotional health. Mindfulness, mentoring and Balint groups are increasing. Reflective practice is a prominent feature in a revised approach to professionalism.Conclusions: There is increasing evidence that the self-care of physicians will impact on patient outcomes. (6, 7) We also have a moral responsibility as educators, faculty and senior clinicians to provide more humane learning and working environments for students and doctors. Innovative educational initiatives are finding ways to overcome significant historical and institutional barriers.References1. Jennings M. Medical student burnout: interdisciplinary exploration and analysis. J Med Humanities. 2009;30(4):253-69. 2. Linzer M, Gerrity M, Douglas JA, McMurray JE, Williams ES, Konrad TR. Physician stress: results from the physician worklife study. Stress Health. 2002;18(1):37-42. 3. Dunn L, Iglewicz A, Moutier C. A conceptual model of medical student well-being: promoting resilience and preventing burnout. Acad Psychiatry. 2008;32(1):44-53.4. Seritan A, Hunt J, Shy A, Rea M, Worley L. The state of medical student wellness: A call for culture change. Acad Psychiatry. 2012;36(1):7-10.5. McWhinney I. Changing models: The impact of Kuhn's theory on medicine. Fam Pract. 1984;1(1):3 -9.6. West CP, Tan AD, Habermann TM, Sloan JA, Shanafelt TD. Association of resident fatigue and distress with perceived medical errors. JAMA. 2009;302(12):1294-300. 7. Wallace JE, Lemaire JB, Ghali WA. Physician wellness: a missing quality indicator. The Lancet. 2009;374(9702):1714-21.
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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 | 0.041 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".