How has Indigenous Health Research changed in Atlantic Canada over two decades? A scoping review from 2001 to 2020
Bibliographic record
Abstract
INTRODUCTION: Indigenous communities across Canada report that transformations in Indigenous health research are needed, where the benefits of research shift intentionally, collaboratively, and with transparency from the researchers directly to Indigenous communities and partners. Despite its challenges and potential for harm, research, if done ethically and with respect and partnership, can be a force for change and will strengthen the efficacy of data on Indigenous Peoples' health and wellbeing. PURPOSE: To characterize the nature, range, and extent of Indigenous health research in Atlantic Canada, and to identify gaps. METHODS: Eleven databases were searched using English-language keywords that signify Indigeneity, geographic regions, health, and Indigenous communities in Atlantic Canada between 2001 and May 2020. All references were reviewed independently by two reviewers. Of the 9056 articles identified, 211 articles were retained for inclusion. Data were extracted using a collaboratively developed data charting form. RESULTS: Indigenous health research in Atlantic Canada has increased over time, covering a diverse range of health topics. The main areas of research included climate change, child and youth health, and food and water security, with the majority of research deriving from Newfoundland and Labrador. Rates of reported community engagement remain relatively low and steady between 2001 and 2020, however there was an increase in researchers seeking Indigenous ethics approvals for such engagement. CONCLUSIONS: This scoping review synthesizes 20 years of Indigenous health research in Atlantic Canada. The results indicate that although there are increases in Indigenous ethics approvals, there is more work needed to ensure that Indigenous Peoples lead, design, and benefit from research conducted in their homelands.
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 | 0.052 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.032 | 0.073 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".