Getting by with a little help from friends and colleagues
Bibliographic record
Abstract
Objective To determine how residents’ relationships with their sources of social support (ie, family, friends, and colleagues) affect levels of burnout and loneliness. Design Cross-sectional survey. Setting Faculty of Medicine at the University of British Columbia in Vancouver. Participants A total of 198 physician-trainees in the university’s postgraduate medical education program. Main outcome measures Residents’ personal and work-related burnout scores (measured using items from the Copenhagen Burnout Inventory); loneliness (measured using a 3-item loneliness scale); and social support (assessed with the Lubben Social Network Scale, version 6). Results Of the 234 respondents who completed the Internet-based survey (a 22% response rate), 198 provided complete information on all study variables and thus constituted the analytic sample. Seemingly unrelated regression analyses indicated that loneliness was significantly ( P < .01) and positively associated with both personal and work-related burnout scores. Greater friend-based and colleague-based social support were both indirectly associated with lower personal and work-related burnout scores through their negative associations with loneliness. Conclusion Social relationships might help residents mitigate the deleterious effects of burnout. By promoting interventions that stabilize and nurture social relationships, hospitals and universities can potentially help promote resident resilience and well-being and, in turn, improve patient 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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".