Strategies for Meaningful Engagement between Community-Based Health Researchers and First Nations Participants
Bibliographic record
Abstract
The Baby Teeth Talk Study (BTT) is a partnership-based research project looking at interventions to prevent early childhood caries (ECC) in First Nations populations in Canada. Community-based researchers (CBRs) conducted preventive and behavioral interventions that targeted expectant mothers and their newborns, over a 3-year period. The work of the CBRs requires a great deal of training and skills to administer the interventions. It also requires a broad set of strategies to meaningfully engage participants to make health-promoting changes in their behavior to prevent ECC in their children. After implementing the intervention, BTT CBRs participated in interviews to explore the strategies they employed to engage participants in the prevention of ECC. CBRs perceived two key strategies as essential for meaningful engagement with BTT participants. First, CBRs indicated that their shared experiences through motherhood, First Nations identity, age, and childhood experience provided a positive foundation for dialog with participants that lead to build trust and rapport. Second, supportive interpersonal and culturally based communication skills of the CBR provided further foundation to engage with participants from a strength-based approach. For example, the CBRs knew how to effectively communicate in ways such as being gentle, non-intrusive, and avoiding any perception of judgment when discussing oral health behavior. In First Nations health research, CBRs can provide an essential link in engaging participants and the community for improvements in health. Researchers should carefully consider characteristics such as shared experience and ability to understand cultural communication styles when hiring CBRs in order to build a solid foundation of trust with research participants.
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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 | 0.139 | 0.182 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.023 | 0.012 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.007 | 0.047 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".