Indigenous Health Counts: Advancing Meta-Analysis Methods For Respondent-Driven Sampling And First Nations, Inuit, And Metis Peoples Living In Urban Areas In Ontario
Bibliographic record
Abstract
Introduction: There is a lack of accurate and valid health information for First Nations, Inuit, and Métis (FNIM) peoples living in urban areas in Canada. Respondent-driven sampling (RDS) is a statistical and sampling technique that allows the health of Indigenous peoples living in urban areas to be examined through the use of their social networks. There are currently no techniques available to pool the results of RDS studies to obtain overall summary-level information across RDS samples. The objective of this dissertation was to develop a meta-analysis technique for RDS data and examine the combined, overall prevalence of key outcomes identified by local Indigenous health service organizations. Data were obtained from the community-led Our Health Counts (OHC) projects – five Indigenous health studies which successfully applied RDS in the cities of Hamilton, Toronto, London, Kenora, and Thunder Bay. Primary outcomes include diabetes mellitus (DM), prescription opioid (PO) use without a prescription or in unprescribed ways, experiences of discrimination in the healthcare system, and use of traditional medicines. Methods: Four manuscripts were completed, including one simulation study and three applications of the results of the simulation study. The first study developed and validated preliminary meta-analysis methods for random effects (RE) and fixed effects (FE) models for RDS data. The subsequent three manuscripts examine the prevalence of (1) DM, (2) PO use without a prescription or out of keeping with the prescription, and (3) experiences of anti-Indigenous discrimination in the healthcare system and use of traditional medicines in FNIM peoples living in urban areas through RE meta-analysis. Results: Using the average variance calculated from RDS-II bootstrap confidence intervals as the estimate of within-study variance for RE and FE models was the only valid meta-analysis method for RDS data. For younger adults, the prevalence of DM was higher among FNIM peoples living in Ontario cities compared to the general population. FNIM peoples living in cities also had a higher prevalence of PO use without a prescription or in unintended ways than the general population. Age differences were found in the prevalence of experiences of anti-Indigenous discrimination in the healthcare system by FNIM peoples living in cities, with younger people reporting more discrimination. Conclusion: RDS is a valuable sampling and statistical technique for examining the health of FNIM peoples living in urban areas in Ontario. Pooling data across OHC sites allows us to obtain a more precise, overall understanding of priority outcomes identified by the Indigenous community partners. Improved understanding allows community partners and decision makers actionable information to tailor programs and interventions to support the needs of Indigenous peoples and to be more effective in improving equity in the healthcare system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".