Cultivating person-centered medicine in future physicians
Bibliographic record
Abstract
Person-centered medicine, while valued implicitly, is not always taught explicitly in medical schools or during residency programs. Threats to educating and practicing person-centered medicine include perceived lack of time, stress, burnout and a paucity of mentors with a systematic approach to modeling and teaching students how to relate to patients in a way that addresses them as whole persons. Herein we review how trainee stress and burnout negatively impact patient care and outline a program designed to teach mindful medical practice that may be an antidote to these problems. Moreover, we present quantitative data and a student’s narrative to highlight how to cultivate person-centered medicine in trainees.Fifty-eight 4th year medical students completed questionnaires pertaining to: depression, burnout, stress, wellbeing, self-compassion and mindfulness before and after taking a 4-week elective entitled, Mindful Medical Practice. Statistically significant improvements were found on emotional exhaustion, depression, self-compassion and mindfulness. One student’s experiences highlighted how what he learned in the elective guided him during his family medicine residency. We conclude with a discussion of how the culture of medicine and the training of future physicians in particular, need to take the whole persons of both the physician and patient into account in order for all to be satisfied with and benefit from medical care.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".