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Enregistrement W3092522942 · doi:10.22605/rrh6112

Sustainability of a rural volunteer program (Nav-CARE): a case study

2020· article· en· W3092522942 sur OpenAlexaffabout
Wendy Duggleby, Barbara Pesut, Grace Warner, Cheryl Nekolaichuk, Lars Hällström, Brittany Elliott, Jennifer Swindle, Sunita Ghosh

Notice bibliographique

RevueRural and Remote Health · 2020
Typearticle
Langueen
DomaineHealth Professions
ThématiqueHealth Policy Implementation Science
Établissements canadiensDalhousie UniversityAlberta Health ServicesUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésMedicineSustainabilityNursing

Résumé

récupéré en direct d'OpenAlex

INTRODUCTION: Nav-CARE (Navigation: Connecting, Accessing, Resourcing and Engaging) is an evidence-based program that was implemented over 1 year in a rural community in western Canada. Nav-CARE uses volunteers who are trained in navigation to facilitate access to resources and provide social support to older persons living in the community with serious illness such as cancer, congestive heart failure and chronic obstructive pulmonary disease. Following implementation in which Nav-CARE was found to be feasible, acceptable and have positive outcomes, Nav-CARE was integrated into the local community-based hospice society program. Two years after a successful implementation, it continued to be sustainable in this same rural community. The purpose of this study was to explore the key factors that facilitated the sustainability of Nav-CARE in a rural hospice society. METHODS: A qualitative single case study design was used with data from several sources collected at different times: (a) pre-implementation, (b) Nav-CARE program implementation (1-year time period), (c) immediately after implementation and (d) 6 months to 2 years after implementation). Data included individual interviews with community stakeholders (n=9), the study volunteer coordinator (n=1), hospice society coordinator (n=1) and Nav-CARE volunteers (n=9). It also included meeting notes of volunteer debriefing sessions and meetings with stakeholders planning for sustainability of Nav-CARE that were held during the 1-year implementation. Data were organized using the i-PARIHS (integrated Promoting Action on Research Implementation in Health Services) framework (a well known implementation framework). Data were analyzed using Yin's qualitative case study approach. RESULTS: The findings from this case study suggested that key factors in facilitating sustainability of a rural community intervention (Nav-CARE) were the organizational context (inner context) and facilitation (facilitator and facilitation processes). Additionally, the inner context included the fit of Nav-CARE with the organization's priorities, the absorptive capacity of the organization, and organizational structure and mechanisms to integrate Nav-CARE into current programs. The hospice society was well established and supported by the rural community. The role of the facilitator and the planned facilitation processes (training of volunteer navigators, ongoing support and planning events) were key factors in the sustainability of the Nav-CARE program. The findings found that the formal role of the facilitator in the implementation and sustainability of Nav-CARE in this rural community required skills and knowledge, as well as ongoing mentorship. As well, the facilitation process for Nav-CARE included formal sustainability planning meetings involving stakeholders. CONCLUSION: Using the i-PARIHS framework and a case study approach, key factors for facilitating sustainability were identified. The role of the facilitator, the facilitation processes and the characteristics of the organizational context were important for the sustainability of Nav-CARE. Future research is needed to understand how to assess and enhance an organization's sustainability capacity and the impact of additional facilitator training and mentoring. This study provides a foundation for future research and adds to the discussion of the issue of sustainability of evidence-based interventions in rural community settings.

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)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,432
Score d'incertitude au seuil0,975

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
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 Opus0,266
Tête enseignante GPT0,620
Écart entre enseignants0,354 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
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

Citations15
Publié2020
Routes d'admission2
Résumé présentoui

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