Examining the role of child welfare worker characteristics and the substantiation decision
Bibliographic record
Abstract
BACKGROUND: The role of child welfare workers is twofold, to promote the safety of children and youth and to address their wellbeing. This provincially legislated mandate requires child welfare workers to make decisions across the child welfare service continuum. After a report of child maltreatment is investigated, workers are required to assess the veracity of the allegation through the substantiation decision and to determine whether the child has been victimized, which may impact on families' future involvement with services. Little is known whether or how individual worker characteristics impact the substantiation decision. OBJECTIVE AND METHODS: This study estimated the degree of variation across caseworker characteristics in the substantiation decision through secondary data analysis of the Ontario Incidence Study of Reported Child Abuse and Neglect (OIS, 2018). We explored how the substantiation decision varied across clinical and caseworker characteristics, using both simple and multilevel logistic regression models. RESULTS: Findings suggest that primarily clinical characteristics predicted the substantiation decision, however, worker years of child welfare experience also predicted substantiation, such that more experienced workers were significantly more likely to substantiate than less experienced workers (est = 0.02, SE = 0.01, p < .10). The Intraclass Correlation Coefficient (35 %) suggests differences among child welfare workers' substantiation decision, they are however, characteristics not measured in this study. CONCLUSIONS: Further research to assess the differential nature of child welfare worker characteristics and their role in decision-making is required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".