Predicting well-being among military health care workers: The role of self-care and leadership
Bibliographic record
Abstract
Introduction: U.S. military health care workers were surveyed regarding individual and organizational factors that might promote professional well-being. Relevant factors can be used as a blueprint for supportive interventions. Methods: Military health care workers from two military treatment facilities were surveyed (N = 204). Individual (i.e., self-care attitudes and behaviours) and organizational (i.e., health-promoting leadership) factors were assessed at baseline, and compassion fatigue, burnout, and effective functioning were assessed at three-month follow-up. Path mediation models assessed 1) the role of self-care behaviours in predicting well-being, 2) the degree to which self-care behaviours mediated the relationship between self-care attitudes and well-being, and 3) whether self-care behaviours mediated the relationship between health-promoting leadership and well-being. Results: The majority (98.5%) of participants regularly engaged in at least one self-care behaviour, and more than half (56.0%) indicated their leaders engaged in at least one health-promoting leadership behaviour sometimes or more frequently. Most respondents (79.0%) reported functioning at, or near, their best; 17.0% scored above cut-off on burnout, and 9.0% scored above cut-off on compassion fatigue. Self-care behaviours independently predicted professional well-being over time, and self-care attitudes indirectly predicted professional well-being through their influence on self-care behaviours. Health-promoting leadership independently predicted self-care behaviours and professional well-being over time. Discussion: Self-care behaviours are associated with better outcomes, and these behaviours are rooted in attitudes. Health-promoting leadership was directly correlated with less compassion fatigue and burnout. A productive self-care mindset and the right climate are critical for optimizing health care worker well-being in the military setting.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".