Confidence and Professional Judgment in Assessing Children’s Risk of Abuse
Bibliographic record
Abstract
Objective: Child welfare agencies have moved toward standardized risk assessment measures to improve the reliability with which child’s risk of abuse is predicted. Nevertheless, these tools require a degree of subjective judgment. Research to date has not substantially investigated the influence of specific context and worker characteristics on professional judgment in the use of risk assessment measures. Method: This research utilized standardized patients performing in scenarios to depict typical child welfare cases. Ninety-six workers interviewed two ‘‘families,’’ completed risk assessment measures, and then participated in interviews regarding their subjective views of their decision making and performance. Results: There was considerable variability in risk appraisals. Confidence in risk assessment performance was related to age, acute level of stress, and the worker’s perceived ability to engage family members. Confidence in risk assessment was further related to case variables. Confidence was not related to level of risk assessed. Conclusion: The variation in risk assessment appraisals in this study, despite at times high rates of worker confidence in their appraisals, speaks to the need for ongoing consultation and increased decision support strategies even among highly skilled and trained 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 | 0.020 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".