Universal Well-Being Assessment Associated With Increased Resident Utilization of Mental Health Resources and Decrease in Professionalism Breaches
Bibliographic record
Abstract
ABSTRACT Background A previous study showed that residents felt a universal well-being visit to a Faculty Staff Assistance Program (FSAP) would increase self-initiated visits. It is unknown whether such program is associated with more self-initiated visits, improved professionalism, or positive well-being measures. Objective We measured internal medicine (IM) resident-initiated visits before and after the universal well-being FSAP intervention to assess for increased utilization of FSAP services and effect on professionalism and well-being measures. Methods Universally scheduled, resident-initiated, program-mandated FSAP visits for IM residents at West Virginia University were counted for years 2014–2019. Professionalism reports of all residents and IM residents were tallied. A Mann-Kendall trend test was used to estimate slope of trends. Burnout and compassion satisfaction (CS) scores were assessed from 2017–2020. Results Residents opted-out of 8 of 239 (3.3%) universally scheduled FSAP visits. Resident-initiated visits significantly increased from 0 in 2014–2015 to 23 in 2018–2019 (slope = 6.5; P = .027; 95% CI [1.0, 8.0]). Program-mandated visits significantly decreased from 12 in 2014–2015 to 3 in 2018–2019 (slope = -2.4; P = .027; 95% CI [-3.0, -1.0]). IM-attributed professionalism reports significantly decreased from 17 of 62 (31%) in 2014 to 1 of 62 (1.6%) in 2019 (slope = -5.7%; P = .024; 95% CI [-11.6%, -0.6%]). Burnout scores remained in the low range (≤ 22) and CS scores in the average-high range (38.7–42) from 2017–2020. Conclusions A universal well-being FSAP program increased resident utilization of mental health resources and was associated with fewer professionalism breaches.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".