Conducting Population Health Intervention Research using Linked Databases: An Evaluation of Home Visiting Programs for At-Risk Families
Bibliographic record
Abstract
ABSTRACTObjectiveThe objective of this population health intervention research was to determine the effectiveness in improving children’s outcomes of a provincial home visiting program for at-risk families in Manitoba, Canada. Home visiting programs have been evaluated in highly structured and supervised conditions which may provide different results than those evaluated in real-world delivery systems. ApproachIn this retrospective cohort study, data for 4,562 children from home visiting program families and 5,184 comparison children were linked to de-identified administrative health, social services, and education data held securely at the Manitoba Centre for Health Policy. Inverse probability of treatment weights were used to address the selection bias inherent in delivering a voluntary program. We used generalized linear modelling to calculate program effects among those exposed to the program, those unexposed and the average effect. Child outcomes examined included being taken into care of child welfare, hospitalizations for maltreatment-related injuries, and child development scores at school entry. ResultsThe home visiting program was associated with lower rates of children being taken into care and lower rates of hospitalization for maltreatment-related injuries. For being taken into care by child’s first birthday, the adjusted Risk Ratio (aRR) was 0.75 (95% Confidence Interval [CI]: 0.66, 0.86); for being taken into care by second birthday, aRR=0.79, (95% CI: 0.70, 0.88); and for hospitalizations for maltreatment-related injuries by third birthday, aRR=0.59 (95% CI: 0.35, 0.99). Similar program effects would be expected among comparison children if they had received the program (i.e., average treatment effect for the untreated). No differences between groups were found across five domains of child development at school entry. ConclusionHome visiting programs can be an effective strategy for decreasing child maltreatment at a population level. Home visiting program enhancements are necessary to improve child development scores when children enter school. Use of population-based linkable data systems provides an opportunity to evaluate interventions using large, real-world samples, adjusting for a wide range of risk factors, and examining a variety of outcomes.
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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 | 0.171 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".