Identifying 'real-world' initiatives for evidence-based physical activity practice: A case study of community-based physical activity programs for persons with physical disability in Canada
Bibliographic record
Abstract
'Real-world' initiatives represent an important information source for evidence-based physical activity (PA) practice; however, accessing this information is often challenging. Casebooks have emerged as an innovative knowledge translation tool for researchers, practitioners, and program participants to address 'research-to-implementation gaps' in PA programming through sharing 'on-the-ground' experiences. To date, several casebooks have been published; yet, remain inconsistent in their methodological approach. The purpose of this project is to provide guidance for search methods that can be adopted for the identification of 'real-world' PA initiatives, and thus, the development of future casebooks. Specifically, search methods were developed to identify community-based PA programming for persons with physical disabilities across Canada. Five distinct peer- and grey literature search sources were included: (1) peer-reviewed literature databases; (2) grey-literature databases; (3) customized Google search engines; (4) targeted websites; and (5) content expert consultation. Screening involved two phases: (1) title screening and hand searching for potentially relevant PA programming information; and (2) full record review to assess program eligibility. In total, 474 potentially relevant programs were identified and 67 met study criteria. The most comprehensive search source was targeted websites, which identified 31 (46.3%) unique programs, followed by content experts (n=15; 22.4%). Only six (9%) unique programs were identified via custom Google searching. No programs were uniquely identified through peer- or grey-literature database searches. This study demonstrates a comprehensive search strategy that serves as a basis for identifying, selecting and critically appraising 'real-world' initiatives that are central to the development of evidence-based PA practices and policies.Acknowledgments: The authors would like to thank Michele Chittenden for her guidance in developing the search strategy. The authors also thank the many content experts who devoted their time to provide valuable information regarding PA programming for persons with disability across Canada. Funding provided by a Social Sciences and Humanities Research Council of Canada Partnership Grant (Canadian Disability Participation Project).
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".