Resident Physician Wellness Curriculum: A Study of Efficacy and Satisfaction
Bibliographic record
Abstract
Background Recent literature highlights the alarming prevalence of burnout, depression, and illness during residency training; a trend that is also linked to suboptimal patient care. Dedicated wellness curricula may be one solution to this concerning issue. Purpose To determine the effect of a multi-faceted wellness curriculum during emergency medicine residency training on wellness scores and to assess resident satisfaction with the program. Methods This study was conducted via a longitudinal survey. In 2009, a faculty-derived resident wellness curriculum (F-RWC) was initiated. This program was then bolstered with a parallel resident-derived curriculum (R-RWC) one year later, in 2010. Emergency medicine residents were surveyed in 2009, 2010, and 2011 to assess wellness at baseline, after one year of the F-RWC, and after one year of combined RWCs, respectively. Surveys included two validated assessment instruments (the Brief Resident Wellness Profile (BRWP) and the SF-8TM Health Survey), a satisfaction Likert scale, and a demographics information sheet. Results The survey response rates were 89% (n=17), 100% (n=17), and 83% (n=24) from 2009, 2010, and 2011, respectively, for a total of 58 participants. From baseline in 2009, there was a significant improvement in resident wellness, with the addition of parallel RWC by 2011, as measured by the BRWP (p=0.024). The faces scale, a subset of the BRWP, showed a trend toward benefit but did not reach statistical significance (p=0.085). There was no evidence of a statistically significant change in SF-8TM scores over time. Participants consistently reported positive satisfaction scores with RWC initiatives. Conclusions Dedicated RWC, with input from both faculty and resident physicians, improved wellness during residency training with a high degree of participant satisfaction. Such programs are needed to support resident physicians during their training.
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".