Bibliographic record
Abstract
The screening decision is one of the most important decisions that child protection agencies make as it determines which families will be subjected to an investigation and can set the stage for further, more intensive involvement. This chapter explores the journey of two Child Welfare organizations in Ontario, Canada, to utilize sensitivity and specificity analysis to support an evidence-based approach to decision-making that balances the risk of not intervening when required with the risk of over-intrusion. The study offers a methodological approach for organizations to examine and monitor their decision-making accuracy. Utilizing Signal Detection Theory, the analysis examined child welfare workers’ decision to escalate community referrals to an investigation by examining the outcome of these decisions. The results demonstrate a shift in the decision-making thresholds related to investigations. At baseline, the tendency to investigate community concerns was high (89% Site 1; 76% Site 2) with a high sensitivity to risk (Site 1 Se = 96%; Site 2 Se = 92%) but a relatively low specificity (Site 1 Sp = 40%; Site 2 Sp = 43%) indicating that more families were subjected to an investigation than necessary. At follow- up, both sites saw an increase in specificity (Site 1 Sp = 48%; Site 2 Sp = 63%) with only a slight decrease in sensitivity (Site 1 Se decreased from 96% to 92%; Site 2 Se 92% to 81%). Given that the false negative cases remained low (Site 1 average = 2% and Site 2 average = 3%), the reduction in risk thresholds was deemed acceptable. Having an approach to data analysis that follows the implementation of such changes over time can assist organizations implementing practice changes to promote continuous learning and improvement while avoiding dramatic swings in the balance of risk.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.213 | 0.034 |
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".