Community Perspectives of Wellness in Manawan, an Atikamekw First Nation Community in Quebec, Canada: A community-based participatory research
Bibliographic record
Abstract
Background: In 2018, the First Nation Atikamekw community of Manawan, in Quebec, participated in a Community Mobilization Training for the promotion of healthy lifestyles. Enhancement of community wellness was chosen as one of the measures to determine the impact of the community mobilization process. Wellness assessments tools tend to focus on measuring wellness at individual levels. Indigenous Peoples understand wellness wholistically and centered on social and natural relationships, and on community, thus wellness assessment should also be centered around these dimensions.Objectives: This research aimed to characterize concepts of wellness from youth, intervention workers, and Elders that could serve for community-specific wellness assessment.Methods: This community-based participatory research project employed concept mapping of wellness statements, which were generated through Photovoice with youth (n=6) and talking circles with intervention workers (n=9) and Elders (n=10). A final set of 84 wellness statements was selected and refined. Participants sorted each statement into thematic groups and rated them based on the priority of addressing the statement and the feasibility of implementing it. Concept maps were created using Concept Systems Global Max software based on sorting proximity and ratings calculations. Participants discussed the results at in-person interpretation sessions and named the wellness concept thematic groups.Findings: Ten thematic groups of statements describe what contributes to community-wellness in Manawan. These are, in priority order: Youth, Community, Infrastructures, Healthy environment, Mobilization, Lifestyles, Culture & traditions, Well-being & identity, Activities on the land, and Community activities. As expected, the perspective of wellness in Manawan is highly wholistic and relational, and the themes obtained are community-specific. Findings are being shared with the community for developing strategies that promote wellness
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".