From policies to reality: External factors influencing delegated child protection workers’ child removal decisions
Bibliographic record
Abstract
Child protection workers are responsible for making complex decisions to protect children from abuse and neglect. Child protection workers receive specialized training on how to make unbiased decisions based on evidence. Yet, external factors can influence the decision-making process. Method: A qualitative study using interpretative description was deployed and eight former child protection workers in northern British Columbia were interviewed. Semi-structured interviews were conducted with an emphasis on the external factors that may influence child protection workers’ decision to remove a child from a legal guardian. Results: Three main themes with 10 subthemes emerged from the data. The first overarching theme was pressure (workload, appearance, politics, and policies). The second overarching theme was hierarchy (management, team leader, team members, and experience). The final overarching theme was resources (support services and placements). The former child protection workers established and clarified external factors that may influence their decision-making process. Conclusions: Several external factors that influenced decision making in relation to the removal of a child from a legal guardian were identified. These findings may help inform professional training for future child protection 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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".