Effective health and wellness systems for rural and remote Indigenous communities: a rapid review
Bibliographic record
Abstract
Background: The Canadian healthcare system bares a long legacy of colonisation and assimilation of Indigenous values and approaches to health and wellness. This system often perpetuates social and health inequities through systemic racism, underfunding, lack of culturally appropriate care and barriers to access care. Current funding legislation policies enacted across federal, provincialand territorial governments do not necessarily uphold Indigenous Peoples’ rights to self-determination, health and wellness. We summarise literature on promising Indigenous health systems and practices that prioritise and/or improve rural Indigenous Peoples’ health and wellness. Objective: The impetus for this review was to provide information on promising health systems, while Dehcho First Nations developed a health and wellness vision. Methods: Documents were gathered from indexed and non-indexed databases to obtain literature from peer-reviewed and non-peer reviewed sources. Two reviewers independently 1) screened titles, abstracts and full texts to ensure they met the inclusion criteria, 2) gathered relevant data from all included documents and 3) identified major themes and sub-themes. Reviewers then discussed and reached consensus on the themes. Results: Thematic analysis revealed six themes for effective health systems for rural and remote Indigenous communities: 1) access to primary care, 2) multi-directional knowledge exchange, 3) culturally appropriate care, 4) training and building community capacity, 5) integrated care and 6) health system funding. Conclusion: Effective health and wellness systems must support Indigenous ways of knowing and doing in healthcare models based on collaborative partnerships with community members, health providers and government agencies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".