Advocating for Implementation of the Global Action Plan on Physical Activity: Challenges and Support Requirements
Bibliographic record
Abstract
BACKGROUND: There is limited understanding of the challenges experienced and supports required to aid effective advocacy of the Global Action Plan on Physical Activity (GAPPA). The purpose of this study was to assess the challenges experienced and supports needed to advocate for the GAPPA across countries of different income levels. METHODS: Stakeholders working in an area related to the promotion of physical activity were invited to complete an online survey. The survey assessed current awareness and engagement with the GAPPA, factors related to advocacy, and the perceived challenges and supports related to advocacy for implementation of the GAPPA. Closed questions were analyzed in SPSS, with a Pearson's chi-square test used to assess differences between country income level. Open questions were analyzed using inductive thematic analysis. RESULTS: Participants (n = 518) from 81 countries completed the survey. Significant differences were observed between country income level for awareness of the GAPPA and perceived country engagement with the GAPPA. Challenges related to advocacy included a lack of support and engagement, resources, priority, awareness, advocacy education and training, accessibility, and local application. Supports needed for future advocacy included guidance and support, cooperation and alliance, advocacy education and training, and advocacy resources. CONCLUSIONS: Although stakeholders from different country income levels experience similar advocacy challenges and required supports, how countries experience these can be distinct. This research has highlighted some specific ways in which those involved in the promotion of physical activity can be supported to scale up advocacy for the GAPPA. When implementing such supports, consideration of regional, geographic, and cultural barriers and opportunities is important to ensure they are effective and equitable.
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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 | 0.001 |
| 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".