Research Done in “A Good Way”: The Importance of Indigenous Elder Involvement in HIV Community-Based Research
Bibliographic record
Abstract
OBJECTIVES: We examined the role that Indigenous Elders can play in ensuring that community-based research (CBR) is conducted ethically. METHODS: We present data from a larger qualitative study exploring ethical issues that occur in HIV-related CBR through the experiences of researchers engaged in CBR. Between May 2010 and July 2011, we interviewed 51 academic and community research team leaders of federally funded HIV CBR studies. We used thematic analysis techniques to identify themes. RESULTS: Participating researchers engage Elders in research because Elders are keepers of Indigenous knowledge, dynamic ethical consultants, community protectors, and credible sources of information who are able to counsel and support, mediate conflict, provide local context and history, and conduct ceremonial roles. Potential challenges cited by participants to engaging Elders in research include finding the right "fit," approaching Elders in a culturally appropriate way, and bureaucratic environments that do not honor Indigenous processes. CONCLUSIONS: Culturally appropriate Elder engagement in HIV CBR with Indigenous communities is vital for promoting positive relationships and culturally safe research that respects ceremony and Indigenous ways of knowing.
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 | 0.131 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.042 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.005 |
| 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".