Predictors of burnout, compassion fatigue, and compassion satisfaction experienced by community health workers offering maternal and infant services in New York State
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.004 |
| 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 it