Physical activity promotion in Manitoba: Strengths, needs, and moving forward
Bibliographic record
Abstract
OBJECTIVES: To present findings of a workshop with physical activity professionals in Manitoba, Canada, to facilitate the enhancement of physical activity promotion efforts by exploring (1) effective physical activity strategies, (2) methods to strengthen physical activity strategies, (3) challenges in implementing physical activity strategies in Manitoba, and (4) strategies to support collaboration. METHODS: The Manitoba Research Chair in Primary Prevention hosted a workshop for 54 stakeholders in Manitoba. Qualitative and quantitative data obtained from the workshop were analyzed using qualitative content analysis and univariate descriptive analysis. Purposive sampling was used to recruit participants with diverse experiences in physical activity promotion. RESULTS: Strategies were identified and presented according to the socioecological model. Community assessment and community engagement, regional partnerships, capacity building, and mitigation of barriers characterized the discussion. In addition, discussions emphasized the need for the consistent and comprehensive application of a provincial physical activity action plan. The workshop ended with a discussion of the importance of collaboration to improve physical activity programs and initiatives. CONCLUSION: Several common needs were identified that reflect topics from the broader literature. Collaborations and insights from workshop participants provide direction to target increased physical activity support and programming across disciplines, sectors, and regions.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 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".