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Record W3118327179 · doi:10.1186/s12913-020-06019-2

Participation in the Cardiovascular Health Awareness Program (CHAP) by older adults residing in social housing in Quebec: Social network analysis

2021· article· en· W3118327179 on OpenAlexafffundabout
Nadia Deville‐Stoetzel, Janusz Kaczorowski, Gina Agarwal, Marie‐Thérèse Lussier, Magali Girard

Bibliographic record

VenueBMC Health Services Research · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsMcMaster UniversityImpactUniversité de MontréalUniversité du Québec à Montréal
FundersUniversité de Montréal
KeywordsAttendanceQualitative researchMedicineGerontologyPsychologySociology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score0.357

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.085
GPT teacher head0.490
Teacher spread0.405 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2021
Admission routes3
Has abstractyes

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