Workplace factors related to health care leader well‐being in rural settings
Bibliographic record
Abstract
PURPOSE: To examine which workplace factors contribute to health care leader well-being in rural settings. METHODS: Working with two rurally focused organizations, we administered a Rural Leader Burnout survey to executive leaders. The survey contained 25 questions; 24 were closed-item multiple choice and 1 open-ended question. The survey was based on the Mini Z 10 item burnout survey with 5 additional items for leaders. Logistic regression and qualitative content analysis determined factors associated with job satisfaction, burnout, and intent to leave (ITL). FINDINGS: There were 288 respondents (response rate 22%). Of 272 with complete data, 61.4% were women and 51.8% had worked > 10 years. About 81% reported job satisfaction, 40.2% were burned out, and 49.8% intended to leave their administrative roles within 2 years. Factors statistically associated with satisfaction were work control (OR = 3.0), values alignment with leadership (OR = 2.1), and trust in organization (OR = 2.0). Work control (OR = 0.3), trust in organization (OR = 0.4), and stress (OR = 4.1) were associated with burnout. Trust in organization (OR = 0.5), feeling valued (OR = 0.6), and stress (OR = 1.8) associated with ITL. Qualitative data revealed three themes relevant to rural leaders: (1) industry challenges, (2) daily operational issues, and (3) difficult relationships. CONCLUSIONS: These exploratory analyses demonstrate practical ways to improve work conditions to mitigate burnout and turnover in rural leaders. Promoting thriving in leaders would be an important step in maintaining the rural health care workforce.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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".