Factors Associated With Burnout in Physical Medicine and Rehabilitation Residents in the United States
Bibliographic record
Abstract
OBJECTIVES: The aims of the study were to determine the prevalence of burnout in physical medicine and rehabilitation residents in the United States and to identify the personal- and program-specific characteristics most strongly associated with residents reporting burnout. DESIGN: This was a cross-sectional survey of US physical medicine and rehabilitation residents. Emotional exhaustion, depersonalization, and burnout were assessed using two validated items from the Maslach Burnout Inventory. Associations of burnout with demographics and personal factors, residency program characteristics, perceived program support, and work/life balance were evaluated. RESULTS: The survey was completed by 296 residents (22.8%), with 35.8% of residents meeting the criteria for burnout. Residents' perception of not having adequate time for personal/family life was the factor most strongly associated with burnout (χ2 = 93.769, P < 0.001). Residents who reported inappropriate clerical burden and working more than 50 hrs/wk on inpatient rotations were most likely to report that they did not have adequate time for personal/family life. Faculty support (χ2 = 41.599, P < 0.001) and performing activities that led residents to choose physical medicine and rehabilitation as a specialty (χ2 = 93.082, P < 0.001) were protective against burnout. CONCLUSIONS: Residents reporting having inadequate time for their personal/family life was most strongly associated with physical medicine and rehabilitation resident burnout, although many personal and program characteristics were associated with burnout.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".