Confidence and Professional Judgment in Assessing Children’s Risk of Abuse
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it