Designing Intersectoral Environments to Address Isolation/Loneliness as a Risk Factor for Dementia: A Mixed Methods Health Promotion Program
Bibliographic record
Abstract
Abstract Background Intersectoral partnerships are critical for effective and sustainable health promotion programs. 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. In this paper, we present initial results on the effectiveness of using shared activities and events to link academic, arts/culture, mental health, and community‐based organizations and generate a sustainable web of resources for, while positively impacting on, the quality of life and connectivity as a protective factor for persons living with Alzheimer’s and related disorders (PLWA) and their carers. Method We used a mixed methods ethnographic approach to describe and measure the impact of shared activities and events on the intersectoral networks by fielding pre‐/post‐partner social network surveys distributed across four social sectors using standard intersectoral network metrics such as density, clustering, centralization, and average path length; as well as measures of centrality and bridging among partners. We measured the impact of this intervention se with pre‐/post‐activity participant surveys using the UCLA Loneliness scale and the CDC health quality of life instrument. All partners and participants answered demographic questions. Result Network measures showed an increase from the initial number of organizations who submitted letters of support for the project (N = 16), as well as increases in network density at the intersectoral level. Patterns of centrality and bridging in the partner data are still emerging, with arts/cultural organizations being the most engaged in facilitating activities and events. For the participants in activities who completed surveys, emerging data demonstrate that those who participated in multiple activities had lower loneliness scores (N = 117 of the 370). Descriptions of context across the project support its upscaling, including the mechanisms that supported the continued growth in number, variety, and modifications of activities despite the impact of Covid‐19 on social distancing. Conclusion Community‐based efforts to create environments to reduce loneliness are possible but require effort to create sustainable intersectoral networks amidst structural and other leadership changes.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".