Striking balance, enjoying challenge : how social workers in child protection stay on the high wire
Bibliographic record
Abstract
Social work scholars have documented the vicarious trauma, burnout and compassion fatigue experienced by social workers involved with abused children with the attendant effects of high absenteeism and worker turnover. However, a mysterious phenomenon exists in child protection. Certain workers not only avoid burnout and survive in their jobs, they thrive. The purpose of this thesis is to describe exploratory research findings, which begin to explain how some career child protection workers avoid burnout, survive and thrive in a chaotic system. The author, using grounded theory methods, reviews the literature and describes the interviews of six "healthy" child protection workers who defy the stress of their work. The research also describes the interviews of two workers who had succumbed to stress. It is discovered from the data that child protection workers balance on a high wire of challenge and like it! Ten sub-processes describe how this balance is achieved despite the difficult work. Risk factors and warning signs threaten their balance, but workers apply two additional processes to steady their balance. In addition, a rare occupational gift is revealed; these career child protection workers love their labour and provide a labour of love. This study has limitations. As a result, only a tentative discussion on policy implications for educators, supervisors, administrators and practitioners is presented. This discussion explores factors that may contribute to decreased absenteeism, increased job satisfaction and perhaps most important, better service to children and families.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.021 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".