Sustaining health education research programs in Aboriginal communities
Bibliographic record
Abstract
Despite evidence supporting the ongoing provision of health education interventions in First Nations communities, there is a paucity of research that specifically addresses how these programs should be designed to ensure sustainability and long-term effects. Using a Community-Based Research approach, a collective case study was completed with three Canadian First Nations communities to address the following research question: What factors are related to sustainable health education programs, and how do they contribute to and/or inhibit program success in an Aboriginal context? Semi-structured interviews and a sharing circle were completed with 19 participants, including members of community leadership, external partners, and program staff and users. Seven factors were identified to either promote or inhibit program sustainability, including: 1) community uptake; 2) environmental factors; 3) stakeholder awareness and support; 4) presence of a champion; 5) availability of funding; 6) fit and flexibility; and 7) capacity and capacity building. Each factor is provided with a working definition, influential moderators, and key evaluation questions. This study is grounded in, and builds on existing research, and can be used by First Nations communities and universities to support effective sustainability planning for community-based health education interventions.
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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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".