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, constructivist theories, and Indigenous methods, 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? A university-community partnership titled the Community Learning Centres (CLC) provided the context for the collective cases. CLC involved the development of three learning centres (CLCs), each of which provided community members with a physical space and online resources pertaining to culturally relevant health education. Semi-structured interviews and a sharing circle were completed with 19 participants, including members of community leadership, external partners, and program staff and users. Document review served to verify information described by participants. Analysis included a description of each case (within-case analysis) and a thematic analysis across cases (cross-case analysis). Seven factors were identified to either promote or inhibit CLC sustainability, including: 1) community uptake (if and how users access the CLC); 2) environmental factors (conditions within the CLC and the community); 3) stakeholder awareness and support (presence and extent of support exhibited by stakeholder groups); 4) presence of a champion (passionate leaders dedicated to CLC success); 5) availability of funding (ability to identify and allocate program funding); 6) fit and flexibility (CLCs’ ability to address user needs and community priorities), and; 7) capacity and capacity building (capacity to sustain the CLC and use learned skills to address other health education issues). These findings were integrated into practical sustainability tools where each factor was provided a working definition, influential moderators, key evaluation questions, and their relationship to other factors. These tools represent the development of a sustainability framework that 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 intervention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".