Association between home visiting interventions and First Nations families’ health and social outcomes in Manitoba, Canada: protocol for a study of linked population-based administrative data
Bibliographic record
Abstract
INTRODUCTION: First Nations people are descendants of Canada's original inhabitants. In consequence of historical and ongoing structural injustices, many First Nations families struggle with challenging living conditions, including high rates of poverty, poor housing conditions, mental illness and social isolation. These risk factors impede caregivers' abilities to meet their children's basic physical and psychosocial needs. Home visiting programmes were developed to support child developmental health in families facing parenting challenges. However, whether home visiting is an effective intervention for First Nations families has not been examined. We are evaluating two home visiting programmes in Manitoba, Canada, to determine whether they promote nurturing family environments for First Nations children. METHODS AND ANALYSIS: This research builds on new and established relationships among academic researchers, government decision-makers and First Nations stakeholders. We will link health, education and social services data from the Manitoba Population Research Data Repository to data from two home visiting programmes in Manitoba. Logistic regression modelling will be used to assess whether programme participation is associated with improved child developmental health, better connections between families and social services, reduced instances of child maltreatment and being taken into out-of-home care by child welfare and reduced inequities for First Nations families. Non-participating individuals with similar sociodemographic characteristics will serve as comparators. We will use an interrupted time series approach to test for differences in outcomes before and after programme implementation and a propensity score analysis to compare differences between participants and non-participants. ETHICS AND DISSEMINATION: Approvals were granted by the Health Information Research Governance Committee of the First Nations Health and Social Secretariat of Manitoba and the University of Manitoba Health Research Ethics Board. Our integrated knowledge translation approach will involve disseminating findings through government and community briefings, developing lay summaries and infographics, presenting at academic conferences and publishing in scientific journals.
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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 | 0.034 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.004 |
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".