Translating an Evidence-Based Physical Activity Service From Context To Context: A Single Organizational Case Study
Bibliographic record
Abstract
Background: SCI Action Canada partnered with researchers to adapt an evidence-based leisure-time physical activity (LPTA) counselling service (Get-in-Motion (GIM). A satellite GIM service called Passez à l’action was established within a French-speaking context for persons with physical disabilities. An understanding of the determinants that infl uenced the implementation and functioning of the GIM service within the Adaptavie context are required to maximize the potential of other community-based LTPA services being successfully introduced in similar organizations. Purpose: The case study objectives are to: 1) describe the characteristics and implementation contexts of two leisure-time physical activity counselling services for Canadians with a physical disability and the adoption process that took place when the protocol was translated to a new context, and 2) elucidate, from the point of view of the service providers, the organizational determinants that could have facilitated and/or hindered the implementation and functioning of these services. Methods: Guided by the Consolidated Framework for Implementation Research, focus groups were held with the directors and staff of each service. Mixed-content and thematic analyses were then used to determine overarching themes. Results: Findings suggest that the presence of service innovators fosters ownership of the service and facilitates ongoing staff training and support. A thoughtful implementation plan should be included as a component of translation between contexts. Conclusions: Lessons learned and recommendations for future translation of similar evidence-based services to additional contexts are discussed.
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.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".