Reach, Adoption, and Implementation Strategies of a Telehealth Fall Prevention Program: Perspectives From Francophone Communities Across Canada
Bibliographic record
Abstract
Introduction . A fall may impact a person’s physical, emotional, and psychological well-being. Fall prevention programs are being implemented to reduce these negative outcomes. However, linguistic barriers in health services may reduce access to such prevention programs. A telehealth fall prevention program was designed to increase access to such programs in French for Francophone minority communities in Canada. This capacity-building project aimed to support community partners to deliver this telehealth program and document strategies used to reach, adopt, and implement the program within various Francophone and Acadian Minority Communities. Methods . A sequential explanatory mixed methodology was used to document reach, adoption, and implementation strategies and describe the lived experiences of program facilitators and organization representatives. Reach, adoption, and implementation were documented and analyzed descriptively, while lived experiences were analyzed using content analysis following the Consortium Framework for Implementation Research. Results . Twelve organization representatives or program facilitators from eight organizations operating in four different provinces participated in the study. Three themes emerged from the qualitative data on reach and adoption: external context, internal context, and capacity building. Four themes were identified as barriers and facilitators to implementation: level of preparation and time management, interpersonal relations and telepresence, exercise facilitation and safety, and technological problem-solving. Conclusion . Using tailored reach and adoption strategies such as prioritizing provinces with higher proportions of needs and training local community program facilitators may lead to the successful implementation of a new telehealth fall prevention program. Results from this study could potentially inform other primary prevention programs or telehealth program implementation.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".