Sinking, swimming and sailing: experiences of job satisfaction and emotional exhaustion in child welfare employees
Bibliographic record
Abstract
ABSTRACT The authors conducted a mixed‐method study after a previous study of child welfare employees revealed a subgroup exhibiting surprisingly high levels of both emotional exhaustion (EE) and job satisfaction (JS). This subgroup included direct service workers, supervisors and managers. As these findings appeared to conflict with previous studies, we re‐reviewed the literature and undertook the current study to account for the co‐existence of EE and JS. We explored and compared this subgroup with two others: workers who found their work satisfying without experiencing high levels of EE and those whose high levels of EE were associated with low JS. Using a survey that included several standardized measures with 226 employees and semi‐structured interviews with a criteria‐based subsample of 25, we explored the role that personality, career expectations, coping styles, stage of life, education, gender and social networks play in outcomes for individual employees. Analyses of quantitative and qualitative data yielded a profile for each subgroup, offering insights into the subjective experiences of workers within individual, social and organizational contexts. These findings have implications for recruitment, training and support of child welfare workers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".