Identifying “real-world” initiatives for knowledge translation tools: 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 source of information for evidence-based practice; however, accessing information about initiatives is often challenging. Casebooks are an innovative knowledge translation (KT) tool for researchers, practitioners, and end-users to address "research-to-implementation gaps" through sharing "real-world" experiences. Several casebooks have been published; yet, they remain inconsistent in their methodological approach for identifying "real-world" initiatives. The purpose of this project is to describe and apply systematic scoping study methods for the identification of "real-world" initiatives relevant for the development of KT tools. Specifically, systematic scoping study methods were developed to identify community-based physical activity (PA) programs for persons with physical disabilities across Canada. To identify PA programs, a search strategy was developed and included five distinct search approaches: (i) peer-reviewed literature databases, (ii) grey literature databases, (iii) customized Google search engines, (iv) targeted websites, and (v) consultation with content experts. Title screening and hand searching identified 478 potentially relevant PA programs. Full record review identified 72 PA programs that met KT tool criteria. The most comprehensive search approach was targeted websites, which identified 25 (35%) unique PA programs, followed by content experts (n = 12; 17%). Only four (5.6%) unique PA programs were identified via custom Google searching. No PA programs were uniquely identified through peer- or grey literature database searches. This study describes and applies a systematic scoping study methodology that serves as a basis for identifying and selecting "real-world" initiatives that are central to the development of evidence-based KT tools.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.026 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".