Burnout and Moral Distress Among Social Workers Working with Children and Families Versus Those Who Do Not
Bibliographic record
Abstract
Objectives: Burnout is of international concern among social workers, and recently moral distress (MD) has been identified among this professional group. Little is known about how burnout and MD experiences differ between social workers serving children and families (CF) and social workers in other domains. Less is known about the potential relationship between burnout and MD across these subgroups of social workers. Methods: This brief report examines if the levels of, and associations, between MD and burnout differ between a sample of Finnish CF social workers (n = 199) compared social workers in other domains (n = 168). Results: Based on multivariate analyses of covariance and hierarchical regression analyses, we found that working with children and families did not moderate the associations between MD and burnout. However, working with children and families was associated with higher levels of exhaustion, MD frequency, and distress. MD frequency and MD distress were also both significant predictors of burnout among the sample of social workers. CF social workers had higher levels of exhaustion compared to the other social workers. Implications: MD may be an important factor influencing the wellbeing of CF social workers. Organizations employing CF social workers are encouraged to investigate potential sources of MD and set workplace policies to reduce risks. More research examining causes of, and identifying effective remedies to, MD is warranted.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".