Participation in the Cardiovascular Health Awareness Program (CHAP) by older adults residing in social housing in Quebec: Social network analysis
Bibliographic record
Abstract
BACKGROUND: The Cardiovascular Health Awareness Program (CHAP) is as a community-based cardiovascular disease prevention program recently adapted to target older adults living in 14 social housing buildings in Ontario (7) and Quebec (7). Social network analysis (SNA) has been used successfully to assess and strengthen participation in health promotion programs. We applied SNA methods to investigate whether interpersonal relationships among residents within buildings influenced their participation in CHAP. METHODS: Our aim was to examine relational dynamics in two social housing buildings in Quebec with low and high CHAP attendance rates, respectively. We used sociometric questionnaires and network analysis for the quantitative phase of the study, supplemented by a phase of qualitative interviews. All residents of both buildings were eligible for the sociometric questionnaire. Respondents for the qualitative interviews were purposively selected to represent the different attendance situations following the principle of content saturation. RESULTS: In total, 69 residents participated in the study, 37 through sociometric questionnaires and 32 in qualitative interviews. Of the latter, 10 attended almost all CHAP sessions, 10 attended once, and 12 attended none. Results of the quantitative analysis phase identified well-known and appreciated local leaders. In Building 1, which had a high attendance rate (34.3%), there was a main leader (in-degree or 'named by others' frequency 23.2%) who had attended all CHAP sessions. In Building 2, which had a low attendance rate (23.9%), none of the leaders had attended CHAP sessions. Results of the qualitative analysis phase showed that residents who did not attend CHAP sessions (or other activities in the building) generally preferred to avoid conflicts, vindictiveness, and gossip and did not want to get involved in clans and politics within their building. CONCLUSION: We identified four potential strategies to increase attendance at CHAP sessions by residents of subsidized housing for older adults: strengthen confidentiality for those attending the sessions; use community peer networks to enhance recruitment; pair attendees to increase the likelihood of participation; and intervene through opinion leaders or bridging individuals.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".