Identifying priorities for sport and physical activity research in Canada: an iterative priority-setting study
Bibliographic record
Abstract
BACKGROUND: There is a need for better alignment between research on sport and physical activity and the needs of those who are in a position to implement the findings. To facilitate advancement and alignment, we identified the top research priorities of sport and physical activity knowledge users from various sectors. METHODS: For this priority-setting study, we used an iterative process of data collection and analysis. Sport and physical activity knowledge users from multiple sectors participated in a workshop (September 2019), which included small working group exercises followed by large-group syntheses leading to the identification of issues that required better understanding. We then sent an online questionnaire to participants for content validation and interim prioritization, to reduce the number of priorities (December 2019 to January 2020). A new questionnaire containing a shortened list of research priorities was sent to an expanded group of respondents to further streamline the list of priorities (January-March 2020). RESULTS: The 24 workshop participants identified 68 issues, of which 21 were retained by the 18 participants in the interim priority-setting questionnaire. The final prioritization questionnaire was completed by 33 stakeholder groups; this step produced a final list of 8 top research priorities. The final priorities identified for sport and physical activity research related to financial support, suboptimal promotion, dropout, best interventions, participation among Indigenous populations, volunteer engagement, safe and inclusive experiences, and knowledge exchange. INTERPRETATION: The 8 priorities identified in this study provide guidance to Canadian sport and physical activity researchers. Research efforts on these priorities will reflect pressing issues as identified by representatives of all sport and physical activity sectors.
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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 | 0.129 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.031 | 0.005 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| 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".