Identifying newborn discharge to child protective services: Comparing discharge codes from birth hospitalization records and child protection case files
Bibliographic record
Abstract
PURPOSE: Newborn removal by North America's child protective services (CPS) disproportionately impacts Indigenous and Black families, yet its implications for population health inequities are not well understood. To guide this as a domain for future research, we measured validity of birth hospitalization discharge codes categorizing newborns discharged to CPS. METHODS: Using data from 309,260 births in Manitoba, Canada, we compared data on newborns discharged to CPS from hospital discharge codes with the presumed gold standard of custody status from CPS case reports in overall population and separately by First Nations status (categorization used in Canada for Indigenous peoples who are members of a First Nation). RESULTS: Of 309,260 newborns, 4562 (1.48%) were in CPS custody at hospital discharge according to CPS case reports and 2678 (0.87%) were coded by hospitals as discharged to CPS. Sensitivity of discharge codes was low (47.8%), however codes were highly specific (99.8%) with a positive predictive value (PPV) of 81.4%, and a negative predictive value (NPV) of 99.2%. Sensitivity, PPV and specificity were equal for all newborns but NPV was lower for First Nations newborns. CONCLUSIONS: Canadian hospital discharge records underestimate newborn discharge to CPS, with no difference in misclassication based on First Nations status.
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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 | 0.007 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".