122 Interventions for indigenous peoples making health decisions: a systematic review
Bibliographic record
Abstract
Introduction Shared decision making (SDM) facilitates collaboration between people and their healthcare providers for informed health decisions. Our review identified SDM interventions to support Indigenous Peoples making health decisions. Methods An Inuit and non-Inuit team of service providers and researchers used an integrated knowledge translation approach with framework synthesis to coproduce a systematic review. We developed a conceptual framework to describe SDM processes and guide identification of studies that describe interventions to support Indigenous Peoples making health decisions. We conducted a search of databases from September 2012 to March 2022, with a grey literature search. Two team members screened and quality appraised included studies for strengths and relevance of studies’ contributions to SDM and Indigenous self-determination. Findings were analyzed descriptively in relation to the conceptual framework. Results Of 5068 citations screened, nine studies reported in ten publications were eligible for inclusion. We categorized the studies into clusters identified as: inclusive of Indigenous knowledges and governance (‘Indigenous-oriented’)(n=6); and based on Western academic knowledge and governance (‘Western-oriented’)(n=3). The studies were found to be of variable quality for contributions to SDM and self-determination, with Indigenous-oriented studies of higher overall quality. A revised conceptual framework reflects four themes: 1) where SDM takes place impacts decision making opportunities, 2) little is known about the characteristics of health care providers who engage in SDM processes, 3) community is a partner in SDM, 4) the SDM process involves trust- building. Discussion There are few studies that report on and evaluate SDM interventions with Indigenous Peoples. Overall, Indigenous-oriented studies sought to make health care systems more amenable to SDM for Indigenous Peoples, while Western-oriented studies distanced SDM from the health care settings. Conclusion Further studies that are solutions-focused and support Indigenous self-determination are needed.
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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 | 0.026 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".