“One size does not fit all” – lessons learned from a multiple-methods study of a resident wellness curriculum across sites and specialties
Bibliographic record
Abstract
BACKGROUND: There is growing recognition that wellness interventions should occur in context and acknowledge complex contributors to wellbeing, including individual needs, institutional and cultural barriers to wellbeing, as well as systems issues which propagate distress. The authors conducted a multiple-methods study exploring contributors to wellbeing for junior residents in diverse medical environments who participated in a brief resilience and stress-reduction curriculum, the Stress Management and Resiliency Training Program for Residents (SMART-R). METHODS: Using a waitlist-controlled design, the curriculum was implemented for post-graduate year (PGY)-1 or PGY-2 residents in seven residency programs across three sites. Every three months, residents completed surveys, including the Perceived Stress Scale-10, General Self-Efficacy Questionnaire, a mindfulness scale (CAMSR), and a depression screen (PHQ-2). Residents also answered free-text reflection questions about psychological wellbeing and health behaviors. RESULTS: The SMART-R intervention was not significantly associated with decreased perceived stress. Linear regression modeling showed that depression was positively correlated with reported stress levels, while male sex and self-efficacy were negatively correlated with stress. Qualitative analysis elucidated differences in these groups: Residents with lower self-efficacy, those with a positive depression screen, and/or female residents were more likely to describe experiencing lack of control over work. Residents with higher self-efficacy described more positive health behaviors. Residents with a positive depression screen were more self-critical, and more likely to describe negative personal life events. CONCLUSIONS: This curriculum did not significantly modify junior residents' stress. Certain subpopulations experienced greater stress than others (female residents, those with lower self-efficacy, and those with a positive depression screen). Qualitative findings from this study highlight universal stressful experiences early in residency, as well as important differences in experience of the learning environment among subgroups. Tailored wellness interventions that aim to support diverse resident sub-groups may be higher yield than a "one size fits all" approach. TRIAL REGISTRATION: NCT02621801 , Registration date: December 4, 2015 - Retrospectively registered.
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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 | 0.145 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 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".