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Enregistrement W4393943526 · doi:10.1186/s12913-024-10853-z

Adaptation and qualitative evaluation of the BETTER intervention for chronic disease prevention and screening by public health nurses in low income neighbourhoods: views of community residents

2024· article· en· W4393943526 sur OpenAlexafffundabout
Mary Ann O’Brien, Aïsha Lofters, Becky Wall, R. Elliott, Tutsirai Makuwaza, Mary-Anne Pietrusiak, Eva Grunfeld, Bernadette Riordan, Cathie Snider, Andrew D. Pinto, Donna Manca, Nicolette Sopcak, Sylvie D. Cornacchi, Joanne Huizinga, Kawsika Sivayoganathan, Peter Donnelly, Peter Selby, Robert Kyle, Linda Rabeneck, Nancy N. Baxter, Jill Tinmouth, Lawrence Paszat

Notice bibliographique

RevueBMC Health Services Research · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueHealth Promotion and Cardiovascular Prevention
Établissements canadiensHealth Sciences CentreSunnybrook Health Science CentreUniversity of AlbertaSt. Michael's HospitalUniversity of TorontoRegional Municipality of DurhamCentre for Addiction and Mental HealthOntario Institute for Cancer ResearchMcMaster UniversityWomen's College Hospital
Organismes subventionnairesCanadian Cancer Society Research InstituteCanadian Institutes of Health Research
Mots-clésPublic healthMedicineFocus groupIntervention (counseling)PovertyNursingCommunity healthQualitative researchHealth administrationNursing researchFamily medicineGerontology

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: The BETTER intervention is an effective comprehensive evidence-based program for chronic disease prevention and screening (CDPS) delivered by trained prevention practitioners (PPs), a new role in primary care. An adapted program, BETTER HEALTH, delivered by public health nurses as PPs for community residents in low income neighbourhoods, was recently shown to be effective in improving CDPS actions. To obtain a nuanced understanding about the CDPS needs of community residents and how the BETTER HEALTH intervention was perceived by residents, we studied how the intervention was adapted to a public health setting then conducted a post-visit qualitative evaluation by community residents through focus groups and interviews. METHODS: We first used the ADAPT-ITT model to adapt BETTER for a public health setting in Ontario, Canada. For the post-PP visit qualitative evaluation, we asked community residents who had received a PP visit, about steps they had taken to improve their physical and mental health and the BETTER HEALTH intervention. For both phases, we conducted focus groups and interviews; transcripts were analyzed using the constant comparative method. RESULTS: Thirty-eight community residents participated in either adaptation (n = 14, 64% female; average age 54 y) or evaluation (n = 24, 83% female; average age 60 y) phases. In both adaptation and evaluation, residents described significant challenges including poverty, social isolation, and daily stress, making chronic disease prevention a lower priority. Adaptation results indicated that residents valued learning about CDPS and would attend a confidential visit with a public health nurse who was viewed as trustworthy. Despite challenges, many recipients of BETTER HEALTH perceived they had achieved at least one personal CDPS goal post PP visit. Residents described key relational aspects of the visit including feeling valued, listened to and being understood by the PP. The PPs also provided practical suggestions to overcome barriers to meeting prevention goals. CONCLUSIONS: Residents living in low income neighbourhoods faced daily stress that reduced their capacity to make preventive lifestyle changes. Key adapted features of BETTER HEALTH such as public health nurses as PPs were highly supported by residents. The intervention was perceived valuable for the community by providing access to disease prevention. TRIAL REGISTRATION: #NCT03052959, 10/02/2017.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,050
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,884
Score d'incertitude au seuil0,978

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0500,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,281
Tête enseignante GPT0,560
Écart entre enseignants0,279 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2024
Routes d'admission3
Résumé présentoui

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