Predictors of burnout, compassion fatigue, and compassion satisfaction experienced by community health workers offering maternal and infant services in New York State
Bibliographic record
Abstract
Although burnout has been increasingly well studied among medical (nurses, physicians, residents) and mental health providers (psychologists, psychiatrists, social workers), there continues to be a lack of attention on the well-being of community-based providers, such as Community Health Workers (CHWs), within the United States. Using cross-sectional data from 75 CHWs employed in 14 agencies funded through the Maternal and Infant Community Health Collaboratives Initiative (MICHC) in New York, our study examined predictors (anxiety, physical health, adverse childhood experiences, job satisfaction, role certainty, demographic and work characteristics) of burnout, compassion fatigue (CF) and compassion satisfaction (CS). Descriptive statistics were used to characterize our sample and linear regression was employed to investigate the correlates of burnout, CF and CS. Results indicated that CHWs with higher levels of anxiety and lower job satisfaction were more likely to have higher burnout scores. CHWs with higher levels of anxiety, lower job satisfaction and fewer days of poorer health were more likely to report higher CF. Those who worked more than 35 h per week were less likely to report higher CS. The study provides recommendations for organizational-level interventions to address risk factors of burnout and CF and promote CS among CHWs, such as bolstering supervision, encouraging greater communication, offering recognition/appreciation of CHWs and creating opportunities for self-care. Findings should be considered when designing organizational-level preventive measures that mitigate burnout and CF and promote CS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".