Drivers of access to cardiovascular healthcare for rural Indigenous Peoples: A Scoping Review
Bibliographic record
Abstract
Context Māori (the Indigenous Peoples of New Zealand) are disproportionately represented in cardiovascular disease (CVD) prevalence, morbidity, and mortality rates, and are less likely to receive evidence-based CVD healthcare. Rural Māori experience additional barriers to treatment access, poorer health outcomes, and a more significant burden of CVD risk factors than Non-Māori and Māori living in urban areas. These inequities are similarly experienced by Indigenous Peoples in other nations impacted by colonisation. Given the scarcity of available literature, a scoping review that explores the barriers and facilitators to accessing cardiovascular care for rural Indigenous Peoples was conducted. Study Design and Analysis The review was underpinned by Kaupapa Māori Research methodology and was conducted utilising Arksey and O’Malley’s (2005) methodological framework. A database search of MEDLINE (OVID), PubMed, Embase, SCOPUS, CINAHL Plus, Australia/New Zealand Reference Centre, and NZResearch.org was used to explore empirical research literature. A grey literature search will also be conducted. Setting or Dataset Literature based in any healthcare providing care to adults for CVD was included. Rural or remote Indigenous Peoples from New Zealand, Australia, Canada, and the United States of America were included. Literature was included if it addressed cardiovascular conditions and reported barriers and facilitators to healthcare access in any care setting. Intervention/Instrument A scoping review that explores the barriers and facilitators to accessing cardiovascular care for rural Indigenous Peoples was carried out to identify and map the extent of research available and identify any gaps in the literature. Results A total of 363 articles were identified from the database search. An additional 19 reports were identified in the grey literature search. Following screening, 14 articles were included from the database search and 3 reports from the grey literature search. The literature is summarised using the Te Tiriti o Waitangi Framework Principles: Tino Rangatiratanga (Self-determination), Partnership, Active Protection, Equity, and Options. Conclusions This scoping review provides unique insights into healthcare access for rural Indigenous Peoples. It may be of use to researchers and health system stakeholders interested in addressing cardiovascular health inequities in rural Indigenous Peoples.
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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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".