A Multi-site Qualitative Study to Explore and Understand Barriers and Enablers Indigenous Community Members Experience When Accessing Health and Social Services
Bibliographic record
Abstract
Background: The current state of health care in Canada for Indigenous Peoples is grounded in the historical colonial development of the existing healthcare system. In an already complicated system, a role such as the Indigenous patient navigator (IPN) can assist to bridge the gap of health inequity. Purpose: The purpose of this study is to understand and explore the barriers and enablers Indigenous community members experience when accessing health and social services from the perspective of the IPN as well as Indigenous community members who access IPN services across health and social care settings in the province of Ontario, Canada. Methods: This multi-site qualitative study was guided by methodological principles of Interpretive Description (ID) (Thorne, 2016) and the Two-Eyed Seeing approach to ensure the inclusion of non-Indigenous and Indigenous worldviews. The framework by Loppie and Wein (2022), will be used to organize the findings of this study. Results: Semi-structured one-to-one, virtual or telephone interviews were conducted involving thirty-six participants (20 IPNs and 16 Indigenous community members). Indigenous community member barriers to access care and enablers to support access to health and social services are described at multiple levels of health determinants including Root, Core, and Stem using a Tree Metaphor outlined and described by Loppie & Wien (2022). Conclusion: This research provides the foundation for future research to explore the role of the IPN and how this role might address the barriers and support enablers Indigenous Peoples experience when accessing health and social services across health care settings.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".