Video for Knowledge Translation: Engaging Older Adults in Social and Physical Activity
Bibliographic record
Abstract
Une vaste majorité des Canadiens âgés ne pratiquent pas suffisamment d'activité physique. Le développement de stratégies originales et innovantes encourageant et appuyant les modes de vie actifs est donc urgent. La vidéo est un outil prometteur pour l'application de connaissances (AC) visant l'engagement de divers publics dans la discussion et l'adoption de comportements favorisant la santé. L'Approche systématique pour les vidéos fondées sur des données probantes (Systematic Approach to Evidence-informed Video, SAEV), qui fournit un cadre pour guider et structurer le développement de vidéos ayant pour objectif l'AC, a été utilisée pour la création et la diffusion d'un documentaire de 19 minutes, I'd Rather Stay (https://vimeo.com/80503957). Quarante-huit participants âgés de 60 ans et plus ont visionné la vidéo, participé à des groupes de discussion et rempli des questionnaires concernant cette vidéo. Les données ont été recueillies après le visionnement et lors d'un suivi organisé six mois plus tard. La vidéo a éduqué, encouragé et mobilisé les personnes âgées sur les questions liées à l'autonomie, à l'activité physique et aux liens sociaux. Nous encourageons les chercheurs à adopter des stratégies d'AC auxquelles les personnes âgées peuvent s'identifier, qui sont accessibles et par lesquelles elles peuvent s'engager à un niveau critique, autant sur le plan émotionnel qu'intellectuel, comme les vidéos basées sur des preuves scientifiques. Most older Canadians do not engage in sufficient physical activity. There is an urgent need for outside-the-box strategies that encourage and sustain active lifestyles. Video is a promising knowledge translation (KT) tool to engage diverse audiences in discussion and action around health promoting behaviours. We adopted a KT framework to inform a structured process of video development we have named systematic approach to evidence-informed video (SAEV). This guided the creation and dissemination of a 19-minute documentary video: I’d Rather Stay (https://vimeo.com/80503957). Following screenings, we collected focus group and questionnaire data from 48 participants aged 60 years and older at baseline and 6-month follow-up. The video educated, encouraged, and activated older people around issues such as independence, physical activity and social connectedness. We encourage researchers to adopt KT strategies – and to use evidence-informed video – that older adults can relate to and critically engage with on an accessible, emotional, and intellectual level.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.013 |
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".