Yarning with remote Aboriginal communities about seeking consent for research, culturally respectful community engagement and genuine research partnerships
Bibliographic record
Abstract
If the invitation to research is not delivered in a way that is understood, or acknowledges that participants may come from a different world-view, it may affect participation rates, and research findings and research relationships with Indigenous communities. Reflections on the consent process and community engagement for research with Indigenous populations are rarely documented as shown in our systematic review.1 In response, The Picture Talk Project was initiated by leaders of remote Aboriginal communities of the Kimberley to have a ‘Yarn’ about the research process.2 Aboriginal leaders formed a partnership with researchers from Sydney and Darwin. Locally respected Aboriginal people were employed to work as Community Navigators and interpret language, provide cultural guidance and learn Western research approaches. Community leaders were interviewed (research topic yarning) and focus groups (collaborative yarns) were held with Aboriginal community members comprising of parents or carers about their understanding of research, the consent process and their preference of how communities should be engaged and how information should be presented. Focus group participants were also given the option to draw pictures on paper during the focus group in order to divert focus to a mutual activity. Transcripts are analysed using NVivo10 qualitative software and coded using inductive and deductive coding with grounded theory. Major Themes are synthesised and supporting quotes from participants were identified. Participants were from different age groups, both males and females and from main local language groups of the Fitzroy Valley, namely Bunuba, Gooniyandi, Walmajarri, Wangkatjungka and Nykina. Interview analysis reveals five main themes: Research – finding knowledge; Showing respect for Aboriginal people, working on country and being flexible with time; Working together with good communication; Reciprocity – learning two ways and Reaching consent. Themes emerging from the focus group data include: Research – your knowledge; Communication – ‘Milli Milli’ (written word) versus Pictures; Community Research Relationships – ‘Checking in’; and Future Directions: What to research next? Aboriginal communities want researchers to change their approach to community engagement. Specific feedback is given on how to communicate in a way that is embraced by remote Aboriginal communities leading to better recruitment rates and more meaningful research outcomes and forming relationships which embody respect for Aboriginal culture.
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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 | 0.346 | 0.302 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.012 | 0.028 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".