Atikamekw Nehirowisiw Mirowatisiwin
Bibliographic record
Abstract
The Atikamekw Nehirowisiw Nation stands out for its strong culture and proactive policies and interventions to ensure the wellness and healing of members of three Nehirowisiw communities. As part of the Kahnawà:ke Schools Diabetes Prevention Project’s Community Mobilization Training study for promotion of healthy lifestyles, we explored perspectives of wellness among members of the Nehirowisiw community of Manawan. This participatory project involved youth, elders, and community intervention workers through an adaptation of the concept mapping methodology outlined by Kane and Trochim. A Photovoice activity followed by photo-elicited talking circles were used to brainstorm how wellness is manifested in the community. Brainstorming was conducted through talking circles for intervention workers and Elders. Over 800 statements about wellness were generated, and similar statements were combined to obtain a final set of 84 statements. Participants sorted the statements in thematic clusters and rated the priority (most important to address) and feasibility (most possible to address) levels of each statement. Statements ranked with high priority and high feasibility were considered strengths of the community. The Nehiromowin language, family relations, the available healing paths, connection to Nitaskinan — the territory — and environmental protection are the community's main strengths. Here we discuss these strengths, and the role they can play in helping the community face a variety of challenges. We present how the process of identifying community strengths can be used in the development of strategic mobilization plans for the promotion of 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".