Seeking consent for research with indigenous communities: a systematic review
Bibliographic record
Abstract
BACKGROUND: When conducting research with Indigenous populations consent should be sought from both individual participants and the local community. We aimed to search and summarise the literature about methods for seeking consent for research with Indigenous populations. METHODS: A systematic literature search was conducted for articles that describe or evaluate the process of seeking informed consent for research with Indigenous participants. Guidelines for ethical research and for seeking consent with Indigenous people are also included in our review. RESULTS: Of 1447 articles found 1391 were excluded (duplicates, irrelevant, not in English); 56 were relevant and included. Articles were categorised into original research that evaluated the consent process (n = 5) or publications detailing the process of seeking consent (n = 13) and guidelines for ethical research (n = 38). Guidelines were categorised into international (n = 8); national (n = 20) and state/regional/local guidelines (n = 10). In five studies based in Australia, Canada and The United States of America the consent process with Indigenous people was objectively evaluated. In 13 other studies interpreters, voice recording, videos, pictures, flipcharts and "plain language" forms were used to assist in seeking consent but these processes were not evaluated. Some Indigenous organisations provide examples of community-designed resources for seeking consent and describe methods of community engagement, but none are evaluated. International, national and local ethical guidelines stress the importance of upholding Indigenous values but fail to specify methods for engaging communities or obtaining individual consent. In the 'Grey literature' concerns about the consent process are identified but no solutions are offered. CONCLUSION: Consultation with Indigenous communities is needed to determine how consent should be sought from the community and the individual, and how to evaluate this process.
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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 | 0.090 | 0.233 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".