Consolidated criteria for strengthening reporting of health research involving indigenous peoples: the CONSIDER statement
Bibliographic record
Abstract
BACKGROUND: Research reporting guidelines are increasingly commonplace and shown to improve the quality of published health research and health outcomes. Despite severe health inequities among Indigenous Peoples and the potential for research to address the causes, there is an extended legacy of health research exploiting Indigenous Peoples. This paper describes the development of the CONSolIDated critERtia for strengthening the reporting of health research involving Indigenous Peoples (CONSIDER) statement. METHODS: A collaborative prioritization process was conducted based on national and international statements and guidelines about Indigenous health research from the following nations (Peoples): Australia (Aboriginal and Torres Strait Islanders), Canada (First Nations Peoples, Métis), Hawaii (Native Hawaiian), New Zealand (Māori), Taiwan (Taiwan Indigenous Tribes), United States of America (First Nations Peoples) and Northern Scandinavian countries (Sami). A review of seven research guidelines was completed, and meta-synthesis was used to construct a reporting guideline checklist for transparent and comprehensive reporting of research involving Indigenous Peoples. RESULTS: A list of 88 possible checklist items was generated, reconciled, and categorized. Eight research domains and 17 criteria for the reporting of research involving Indigenous Peoples were identified. The research reporting domains were: (i) governance; (ii) relationships; (iii) prioritization; (iv) methodologies; (v) participation; (vi) capacity; (vii) analysis and findings; and (viii) dissemination. CONCLUSIONS: The CONSIDER statement is a collaborative synthesis and prioritization of national and international research statements and guidelines. The CONSIDER statement provides a checklist for the reporting of health research involving Indigenous peoples to strengthen research praxis and advance Indigenous health outcomes.
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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 | 0.725 | 0.804 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.038 | 0.037 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.024 | 0.011 |
| Open science | 0.021 | 0.021 |
| Research integrity | 0.024 | 0.023 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".