Identifying “real-world” initiatives for knowledge translation tools: a case study of community-based physical activity programs for persons with physical disability in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 it