Designing Attitudinal Environments to Address Stigma: Virtual Film Screenings and Panel Discussions on Dementia in a Health Promotion Program
Bibliographic record
Abstract
Abstract Background Intersectoral partnerships are critical for effective and sustainable health promotion programs. Yet stigma and misperception continue to interfere with public engagement in dementia prevention programs, outreach, and willingness to access to dementia and‐or mental health related services for persons living with Alzheimer’s and‐or related disorders and personal carers. The aim of our Public Health Agency of Canada Dementia Community Investment project, What Connects Us∼Ce Qui Nous Lie (2020‐2023), was to collaboratively cultivate sociocultural environments worth living in using shared activities and events to address stigma at the intersection of dementia, aging and mental health. In this paper, we present initial results on the effectiveness of using on‐line, community screenings of short films featuring stories about living with dementia, followed by curated panel discussions with the film directors, arts/culture partners, policy makers and other stakeholders. Method We employed a mixed methods ethnographic approach to describe and measure the impact of six film screenings with pre/post‐film and then later post‐discussion measures of semantic sentiment. All discussions were simultaneously interpreted, and based on open‐ended qualitative questions that had been developed with panelists prior to the screening. The qualitative questions asked for one word/phrase responses to generate word clouds in French and English. Three researchers then assessed the words/phrases until agreement on positive or negative value was reached. French words were back‐translated into English prior to analysis using suzy‐net, a natural language processor, to confirm positive/negative valence and to measure changes within each film screening and across all film screenings. Result There was a significant change in sentiment from negative (e.g., frustration, lies, loss, confusion) to positive sentiment (e.g., humanity, empathy, compassion, understanding) across all waves within and across the six screenings, with the most change occurring post‐discussion. In addition, partners and project‐related team members were removed from the analysis to test for partner participation confounding with the overall shift in valence remaining the same. Conclusion Films about living with dementia, and curated questions developed in collaboration with panelists and directors of 1 st person experiences can positively impact changes in sentiment about dementia.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| 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".