A Longitudinal Analysis of the Families First Screening Program in Manitoba, Canada: Cleaning, Validating and Linking via Health Registry Data
Bibliographic record
Abstract
IntroductionManitoba Public Health Nurses (PHNs) attempt to visit all families with newborns shortly after discharge from birth hospitalizations. Since 2000, PHNs have completed the Families First Screen (FFS) at these visits, to identify families at risk for child maltreatment. The information captured in FFS is a valuable tool for research. Objectives and ApproachOur objective was to clean and validate FFS data and link to health data in the Manitoba repository in order to determine the percent of births in Manitoba hospitals that had FFS. We identified all babies born in Manitoba hospitals 2000-2015 using ICD-9-CM /ICD-10-CA codes. Mothers were identified through the Health Registry (Mom_Baby Link File) using scrambled Personal Health Identification Numbers (sPHINs). FFS data were linked to births via baby’s sPHIN. Determining which FFS records linked to babies required several steps of cleaning and validating the data to account for differences in birthdates between files, missing sPHINs, and multiple records. ResultsFor example, in 2014 there were 16,079 births and 14,002 FFS records; 13,524 FFS had mother and/or baby sPHIN. For those missing baby sPHIN (9,295), 99.8% were retrieved via the Mom_Baby Link File. Linking the FFS to the hospital births we found: 3,043 births didn’t have an FFS; 12,762 had a single FFS, and 274 births had multiple FFS (i.e., baby associated with more than one mother, FFS and/or form date). To ensure that the baby was only associated with one mother and one FFS the most current FFS was kept. We found that in 2014, 81.07% (13,036/16,079) of the births had an FFS. In the longitudinal analysis, the percent of births with an FFS ranged from 74.6% in 2000 to 81.1% in 2014. Conclusion/ImplicationsWe were able to achieve good linkage between FFS and health registry data, allowing this rich data source to be used for research on maternal and child health. Information on percent of births with FFS has been shared with policy-makers over the years and changes to screening practices implemented.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.022 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".