Indigenous Health Counts: Advancing Meta-Analysis Methods For Respondent-Driven Sampling And First Nations, Inuit, And Metis Peoples Living In Urban Areas In Ontario
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».