Using Administrative Data to Evaluate the Effectiveness of Home Visiting Programs for Improving the Well-Being of First Nations Children and Parents
Bibliographic record
Abstract
IntroductionThe province-wide Families First Home Visiting Program (FFHV) provides home visiting to families with children living in conditions of risk. It remains unknown if First Nations families are benefiting from the program. Using existing administrative and population-wide data is an innovative practice to evaluate programs that have been scaled up. Objectives and ApproachThe objective is to determine FFHV’s effectiveness at improving outcomes for First Nations children and parents. The partnership with First Nations Health and Social Secretariat of Manitoba facilitated access to First Nations identifiers and provided guidance in conducting the study. Program data from 4,010 First Nations children and parents were linked at an individual-level to administrative data housed at the Manitoba Centre for Health Policy. We compared the predictive probability of outcomes of program and non-program families. Inverse probability of treatment weights were used to adjust for confounders related to both entry into FFHV and the outcomes under study. ResultsThe cohort of First Nations children and parents was successfully linked through an individual scrambled health identifier. FFHV was associated with higher rates of child immunization at age one (71% versus 66%) and age two (47% versus 41%) and parental involvement in community support groups (21% versus 17%). It was also associated with lower rates of being in care of child welfare at age one (10% versus 14%) and age two (15% versus 19%); maltreatment-related hospitalizations at age three (0.4% versus 1.0%); and child victimization as measured by justice system records (1.7% versus 3.0%). However, there were no differences in being “not ready for school” between the two groups of children, nor between the groups of mothers in physician visits for mental health reasons. Conclusion/ImplicationsHome visiting services can play a role in supporting healthy development of First Nations children by providing support to parents and connecting children to health and social services, however, there also remains an urgent need for long term strategies to address structural inequality and the ongoing effects of colonization.
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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 | 0.021 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".