A Multilevel Examination of Whether Child Welfare Worker Characteristics Predict the Substantiation Decision in Canada
Bibliographic record
Abstract
The decision to substantiate a report of child maltreatment represents a key decision point in the child welfare service decision-making continuum. This decision has various potential implications for children and their families, which may include more intensive child welfare involvement or the cessation of services. The substantiation decision is determined by whether there is enough evidence to suggest that maltreatment or the risk of maltreatment has occurred. To date, there has been minimal exploration of whether child welfare worker characteristics might influence this critical decision point. The Decision-Making Ecology would suggest that indeed, worker characteristics play a role in how they carry out their role. Given the importance of this decision point, this study uses secondary data to examine whether worker characteristics, such as education level and type, ethnoracial identity, caseload, and experience, predict substantiation in the Canadian child welfare context. Furthermore, this study utilizes multilevel modeling, a theoretically important and unique method of analyzing organizational data that considers differences in decisions among child welfare workers. The final model included 4,327 children and 567 workers from across Canada. Several case level factors (e.g., child age and functioning, caregiver risk factors) predicted the substantiation decision. Furthermore, and most importantly for this study, worker characteristics significantly predicted their substantiation decision. Workers with fewer years of experience, those in an Ongoing Services role, and with a lower caseload substantiated significantly more often than those with more work experience, in another role, and with higher caseloads. Lastly, caseload and years of experience, and training and caseload both interacted to predict the substantiation decision. Implications for policy and practice and future research areas are discussed.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".