Families First Home Visiting programme reduces population-level child health and social inequities
Bibliographic record
Abstract
BACKGROUND: Home visiting has been shown to reduce child maltreatment and improve child health outcomes. In this observational study, we explored whether Families First, a home visiting programme in Manitoba, Canada, decreased population-level inequities in children being taken into care of child welfare and receiving complete childhood immunisations. METHODS: De-identified administrative health and social services data for children born 2003-2009 in Manitoba were linked to home visiting programme data. Programme eligibility was determined by screening for family risk factors. We compared probabilities of being taken into care and receiving immunisations among programme children (n=4575), eligible children who did not receive the programme (n=5186) and the general child population (n=87 897) and tested inequities using differences of risk differences (DRDs) and ratios of risk ratios (RRRs). RESULTS: Programme children were less likely to be taken into care (probability (95% CI) at age 1, programme 7.5 (7.0 to 8.0) vs non-programme 10.0 (10.0 to 10.1)) and more likely to receive complete immunisations (probability at age 1, programme 77.3 (76.5 to 78.0) vs non-programme 73.2 (72.1 to 74.3)). Inequities between programme children and the general population were reduced for both outcomes (being taken into care at age 1, DRD -2.5 (-3.7 to 1.2) and RRR 0.8 (0.7 to 0.9); complete immunisation at age 1, DRD 4.1 (2.2 to 6.0) and RRR 1.1 (1.0 to 1.1)); these inequities were also significantly reduced at age 2. CONCLUSION: Home visiting programmes should be recognised as effective strategies for improving child outcomes and reducing population-level health and social inequities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".