I’D RATHER STAY: DOCUMENTARY VIDEO AS A KNOWLEDGE MOBILIZATION TOOL FOR AGE-SUPPORTIVE NEIGHBORHOODS
Bibliographic record
Abstract
We created “I’d Rather Stay,” a 19-minute, evidence-informed documentary video, to engage community and government stakeholders around barriers and facilitators for age–supportive neighborhoods. We used the interaction model of knowledge translation to critically assess two dissemination stages; phase one (societal level): 14 forums with government, policy makers, and older adults, and 7 international film festivals with the general public (N= est. 800); phase two (individual level): screening and focus groups with 10–15 older adults in 6 different geographic locations (average age= 73). During phase one we analyzed video Director and researcher field notes, and post-screening discussion content. In phase two we analyzed field notes, focus group transcriptions, and post-screening and 6-month follow-up surveys. In both phases, we found that documentary video effectively educated viewers and initiated discussion. Further, results from our extended data collection and analysis in phase two suggests that individuals were impacted along a scale: education, knowledge diffusion (sharing), and/or action to improve circumstances. We also offer insight on strategies to move research-evidence from discussion to 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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".