TRANSLATING RESEARCH INTO PRACTICE USING PATIENT-CENTRED VIDEOS: DEVELOPMENT AND ANALYSIS OF UPTAKE
Bibliographic record
Abstract
Purpose: We used patients input and behaviour change theory to design a video series on the Too Fit To Fracture physical activity recommendations. The aim of this work is to describe series development and report on uptake. Methods: Focus groups and interviews were conducted with older adults across Ontario, with purposeful sampling by gender and urban/rural location. Two researchers coded data and identified emerging themes, categorized as representing capability, opportunity and motivation in accordance with the Behaviour Change Wheel. Themes informed a 13-part video series featuring patient stories, answers to common questions, and functions: modeling, persuasion, training, incentivisation, education and enablement. Videos featured cases of variable age and gender, and addressed noted barriers or patient questions. Media communications were the primary delivery method. Uptake over 7 months was estimated as views in total and by region. Results: Since their release in November 2015, videos were shared by the Canadian Society for Exercise Physiology, Osteoporosis Canada, American Society for Bone and Mineral Research and the International Osteoporosis Foundation, and in traditional and social media. Videos were viewed 20,800 times in 86 countries. Audiences were primarily in Canada (16898 views, 81% of total) and the United States (2060 views, 10% of total) and other English-speaking countries (744 views, 4% of total). Average duration of views in English-speaking countries was 78% compared to 60% elsewhere. Within Canada, rural residents accounted for 22% of the viewership, slightly above the proportion of rural Canadians (19%). Nearly half of views were within the month of release when promotion was active. Another spike came after traditional media articles about the work of one of the authors and cited the videos. Conclusions: Partnering with knowledge users to create patient-centred, theory-informed, educational tools and delivery strategies resulted in broad uptake.
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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 | 0.102 | 0.312 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".