Risk of Future Maltreatment: Examining Whether Worker Characteristics Predict Their Perception
Bibliographic record
Abstract
Child welfare workers aim to promote the well-being and safety of children and are the link between the child welfare system and families. Families served by the child welfare system should expect similar service based on clinical factors, not based on their caseworker's characteristics. Using secondary data analyses of the most recent Canadian Incidence Study of Reported Child Abuse and Neglect (CIS-2008) and multilevel modeling, this study examines whether child welfare worker characteristics, such as education level and field, age, and experience predict their perception of the risk of future maltreatment. A total of 1729 case-level investigations and 419 child welfare workers were included in this study. Several one-level logistic regression and two-level logistic regression analyses were run. The best-fit model suggests that caseworkers with a Master's degree, more than 2 years of experience, and more than 18 cases were significantly more likely to perceive risk of future maltreatment. Further, the interaction between degree level and age also significantly predicted the perception of risk of future maltreatment. Results suggest that the perception of risk of future maltreatment may be influenced by caseworker factors, thus service to families may differ based on caseworker characteristics.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".