Child Welfare Workers’ Decision-Making in Child Sexual Abuse Investigations
Bibliographic record
Abstract
Child sexual abuse (CSA) remains one of the most harmful types of child maltreatment, with many victims experiencing negative consequences across the lifespan. When allegations of CSA are referred to child protection services and an investigation is deemed necessary, child welfare workers must assess whether or not the allegations are founded. The objective of this thesis was to explore child welfare workers’ decision-making over the course of a child sexual abuse investigation. The thesis used a mixed methods approach, to better understand the factors workers consider when assessing child sexual abuse, when substantiating an investigation and in turn when making referrals to ongoing services. Three papers form the basis of this dissertation. The first paper provided a profile of CSA investigations reported to child welfare authorities in Ontario, Canada and examined the substantiation decision using chi-square and CHAID analyses. Paper two, used chi-square and CHAID analyses to examine whether case and caseworker factors influenced the decision to transfer a child/family for ongoing services following a CSA investigation. Paper three is a qualitative study, whereby child welfare workers were interviewed about their experiences completing CSA investigations to better understand the factors that influence their decision-making. Overall findings highlight that case factors such as the child’s disclosure statement, age, gender, perpetrator of abuse, subtype of abuse, and certain child functioning issues, such as internalizing and externalizing behaviours, influence a worker’s substantiation decision and the decision to transfer to ongoing services. Caseworker factors, such as experience in the role, intuitive reasoning, and bias, can impact decision-making. Organizational aspects, such as caseloads, timeframes, and supervisory support, also contributed to the overall decision-making process. The conclusions of this study highlight the many complexities workers face when making such consequential decisions. Recommendations for future research and implications for practice are provided.
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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 | 0.033 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".