Who is research serving? A systematic realist review of circumpolar environment-related Indigenous health literature
Bibliographic record
Abstract
BACKGROUND: Addressing factors leading to health disparities in the Circumpolar North require approaches that consider and address the social determinants of health including on-going colonization. Today, colonization and related policies and processes, continue to manifest in the marginalization of Indigenous knowledge, particularly its use in research; however, Indigenous populations have moved from being research subjects to leaders and consumers of environmental health research. Given the tensions that exist between how health research is conducted, how the results are mobilized, and who has control and access to the results, we examine how peer-reviewed environment-related Indigenous health research in the Circumpolar North is serving the needs of Indigenous communities, governments, and organizations. METHODS: A modified systematic-realist literature review was conducted. Three databases were searched for peer-reviewed literature published from 2000 to 2015. Articles were included if the research focused on the intersection of the environment and health in Northern Canada and/or Alaska. A total of 960 unique records were screened for relevance, and 210 articles were analysed. RESULTS: Of these relevant articles, 19% discussed how Indigenous peoples were engaged in the research. There was a significant increase in reporting participatory, community-based methods over time; the proportion of articles reporting community-engagement varied by research topic; quantitative research articles were significantly less likely to report community-engaged methods; and most articles did not clearly report how the results were shared with the community. CONCLUSION: The results raise a number of questions for the field of Circumpolar environment-related Indigenous health research, including whether or how authors of peer-reviewed literature should (or should not) be obliged to describe how research is serving Northern Indigenous communities. The results are intended to stimulate further conversations and bridge perceived dichotomies of quantitative/qualitative, Western/Indigenous, and empirical/community driven research approaches, as well as underlying assumptions that frame health research.
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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 | 0.092 | 0.264 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.043 | 0.031 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".