Implementation of the Cardiovascular Health Awareness Program (CHAP) for adults on a waiting list for a family physician
Notice bibliographique
Résumé
<title>Abstract</title> <bold>Background</bold>: The Cardiovascular Health Awareness Program (CHAP) was originally developed and evaluated as a community-based cardiovascular disease (CVD) prevention program in communities where access to family physicians was not a significant issue. Many Canadians now face sub-optimal access to a regular source of primary healthcare. Centralised waiting lists and prioritization based on urgency of medical need were created to address this problem. Our objective was to assess the potential benefits of offering a modified version of the CHAP program to adults on the waiting list.<bold>Methods</bold>: The implementation was conducted in Laval (Canada), targeting individuals 40 years of age or older who were registered on the waiting list (GACO) and had a priority code of 3. Participants were invited through a personalized letter to attend sessions in community health centres. The sessions were facilitated by trained volunteers and supervised by a nurse. During the sessions, participants completed CVD risk profiles, risk of type 2 diabetes questionnaire (CANRISK); had their blood pressure, height and weight as well as waist circumference measured; received targeted healthy lifestyle and patient education materials; and were referred to local programs including a medical follow-up, when required. <bold>Results</bold>: A total of 2,036 invitation letters were sent resulting in 281 (14.2%) participants attending one of 26 3-hour sessions held in 4 community health centres, assisted by 33 volunteers and supervised by a nurse. The average age of attendees was 58.1 (SD=8.2) and a majority were female (58%, n=163). Most participants were sedentary (57.3%, n=161), had low fruit and vegetable consumption (54.4%, n=153), and were overweight (38.9%, n=109) or obese (41.7%, n=117). A third of participants (34.2%, n=96) had BP ≥140/90 and 11.4% (n=32) were classified as having a very high risk for developing diabetes. Over 40% (n=117) of participants were referred to health promotion programs offered by local health authorities and 4.6% (n=13) were referred to family physicians or emergency departments (1.8%, n=5) for short-term medical assistance.<bold>Conclusions</bold>: Many Laval adults on a waiting list for a family physician would greatly benefit from having a regular source of primary healthcare to prevent, delay, and manage their cardiovascular health.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».