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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".