Physical activity–based, wholistic, wellness interventions for Indigenous women in Canada: An environmental scan to identify programs and promising practices
Bibliographic record
Abstract
OBJECTIVES: This environmental scan has two aims. The first is to identify wholistic, physical activity (PA)-based wellness interventions for Indigenous women in Canada through the completion of a scoping review of published and grey literature and key informant interviews. The second is to identify promising practices and potential barriers to intervention development and delivery. METHODS: Components of the environmental scan included (1) the creation of a logic model in collaboration with a community-based Advisory Group, (2) a scoping review of wholistic PA-based wellness interventions between January 1990 and March 2022, (3) key informant interviews of individuals involved in PA-based wellness programming, and (4) thematic analysis of promising practices, enablers, and barriers found in both the articles and interviews. SYNTHESIS: The scoping review identified 16 interventions. Through key informant interviews, 6 additional interventions were identified. Programs were largely community-based and culturally appropriate. Financial factors were the most common barrier. Walking as a form of PA was commonly employed and key informant interviews highlighted the importance of exploring on-the-land activities, group activities, and the incorporation of all elements of wholistic health (mental, physical, spiritual, and emotional). Only one study employed Indigenous research methods. CONCLUSION: There is limited published literature and a dearth of PA-based, wholistic health programs for Indigenous women in Canada. More programming and research are required to address the unique health needs of Indigenous women and mitigate the legacy impacts of colonization on health.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".