Applying a police engagement model to child protection social work: a viable option for British Columbia?
Bibliographic record
Abstract
Child welfare services seek to help children, youth, and families following an event of child abuse or neglect, as well as attempt to prevent such events from occurring. However, the research gathered in this study suggests that child welfare intervention is rarely perceived as helpful by the general public. This perception has damaging impacts for the children, youth, and families that the system aims to protect, as well as for the workers offering these services. Another helping profession that has experienced comparable challenges is law enforcement. As such, this major paper explores initiatives previously undertaken by police to help repair their relationship with the community and reviews the outcomes that these efforts have generated. In an effort to unveil pathways of reconciliation between child welfare services and the community, interviews with professionals in the field were conducted to establish if the same initiatives used by police services could be adopted by child welfare services. The results of this study suggest that applying community policing and problem-oriented policing models to child protection social work would be beneficial. Based on the findings of this study, this paper recommends that applying community engagement strategies used by police agencies is a viable option for reform to child welfare services in British Columbia. Furthermore, this paper seeks to contribute to the growing field of research on bridging the gap between social work, law enforcement, and the community.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".