Comprehensive evaluation of a three-component psychosocial protocol used to guide decision-making for child maltreatment concerns, developed for medical settings
Bibliographic record
Abstract
Canadian hospitals lack assessment instruments for making decisions about child maltreatment. The purpose of the present study was to conduct a comprehensive evaluation of a psychosocial protocol that was developed for a hospital Suspected Child Abuse and Neglect (SCAN) team to assist in the assessment and reporting of suspected child maltreatment cases. The psychosocial protocol included a referral form, an injury assessment, and a parent-child relational interview. The central finding of this study was that several demographic variables, as well as the Psychosocial Injury Assessment (PIA) and the Empirically-Based Clinical Decision-Making Interview (ECDMI) constructs were related to case outcome (referral to CAS). In terms of decision sensitivity and specificity, using set clinical cut-offs on the PIA and ECDMI, most led to classifications that matched those of expert raters. Furthermore, a validity check of the ECDMI found that risk ratings could differentiate the high-risk SCAN sample from a low-risk community sample. Other findings of the study were that characteristics of cases reviewed for maltreatment in this hospital setting differed from what is typically found in the child maltreatment literature. Most notably, children tended to be under the age of two with married, well-educated parents. Overall, this study supports the beginnings of a reliable and valid psychosocial protocol to improve decision-making in hospital settings.
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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 | 0.043 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".