Motivation, Satisfaction and Perceived Benefits of Volunteering With the Cardiovascular Health Awareness Program (CHAP): A Survey of French-speaking Volunteers in Quebec
Notice bibliographique
Résumé
Abstract Background: Locally recruited and trained volunteers are a vital component of the Cardiovascular Health Awareness Program (CHAP), a community-based cardiovascular disease prevention program that has been implemented across different settings and targeting different populations. As part of expanding and adapting CHAP to new settings, we conducted a pragmatic cluster randomized controlled trial to implement CHAP targeting elderly residents of subsidized social housing buildings in Québec and Ontario. As there is some evidence that the culture of volunteering might be different for English-speakers than their French-speaking counterparts, our objective was to generate a profile of French-speaking CHAP volunteers and to compare it with English-speaking volunteers in Ontario who participated in an earlier community-based CHAP program and with volunteers in Canada as reported in the Statistics Canada's General Social Survey on Giving, Volunteering and Participating (CSGVP). Methods: A cross-sectional survey was administered online or by telephone to volunteers who participated in at least one CHAP session held in subsidized social housing in Montérégie (Québec, Canada) between March 2018 and June 2019. Results: The response rate was 79% (30/38), majority were female (87%, 26/30), retired or semi-retired (83%; 25/30), almost two-thirds (63%, 19/30) had previous work experience in the healthcare field, and 40% (12/30) had more than 10 years of volunteering experience. These characteristics were similar both to those of general profile of volunteers in Canada and to English-speaking volunteers in Ontario who participated in an earlier pharmacy-based CHAP program. The top 5 reasons for volunteering among French-speaking CHAP volunteers were identical to those reported in CSGVP: making a contribution to the community; using one’s skills and experience; improving one’s sense of well-being or health; exploring one’s own strengths; and networking with or meeting people. The majority of French-speaking CHAP volunteers, like their English counterparts in Ontario, reported a high level of satisfaction with their participation in the program, enjoyed their interaction with participants, and gained new knowledge. Conclusions: French-speaking CHAP volunteers in Quebec share many similarities with English-speaking CHAP volunteers in Ontario and volunteers in Canada in terms of background, motivation, satisfaction and perceived benefits of volunteering.
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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».