Ontario child protection workers' views on assessing risk and planning for safety in exposure to domestic violence cases
Bibliographic record
Abstract
Abstract The use of standardized tools to assess risk for children is mandatory in the child protection sector in Ontario. Factors that can be used specifically to assess the risk of lethality in exposure to domestic violence (DV) cases, however, are largely missing from these tools. Using data from an online survey of 138 child protection workers in Ontario, the current study examines practitioners' risk assessment and safety planning practices with DV cases. Findings provide an overview of the frequency of risk assessment and management strategies within various environmental contexts (e.g., urban and rural) and populations (e.g., indigenous and immigrants/refugees). According to the practitioners sampled, assessing and managing risk are frequently and consistently completed across the province, although specific strategies and challenges vary. Although mandatory provincial child protection tools are commonly used, some workers report using other specific DV risk assessment tools to complement their own measurement of risk and planning for safety. Respondents emphasized the importance of working collaboratively with families and professionals in other sectors to address risk. Implications for future research include exploring the barriers and challenges of using DV‐specific risk assessments in child protection and factors contributing to these challenges as identified by practising 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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".