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Enregistrement W4388790945 · doi:10.22605/rrh8294

Exploring the ideas of young healthcare professionals from selected countries regarding rural proofing

2023· article· en· W4388790945 sur OpenAlexafffund
Ian Couper, Manoko Innocentia Lediga, Ndivhuho Beauty Takalani, Mayara Floss, Alexandra E Yeoh, Alexandra Ferrara, Amber Wheatley, Lara Feasby, Marcela Araújo de Oliveira Santana, Mercy Wanjala, Sneha P Kotian, Veronika Rasic, Vuthlarhi Shirindza, Alan Bruce Chater, Theadora Swift Koller

Notice bibliographique

RevueRural and Remote Health · 2023
Typearticle
Langueen
DomaineHealth Professions
ThématiqueGlobal Health Workforce Issues
Établissements canadiensIsland Health
Organismes subventionnairesUniversiteit StellenboschGovernment of CanadaWorld Health Organization
Mots-clésFocus groupThematic analysisHealth careContext (archaeology)Qualitative researchRural areaNursingPublic relationsRural healthMedicineMedical educationPolitical sciencePsychologySociologyGeographySocial science

Résumé

récupéré en direct d'OpenAlex

INTRODUCTION: Globally, most countries struggle to meet the health needs of rural communities. This has resulted in rural areas performing poorly when compared to urban areas in terms of a range of health indicators. There have been few coherent or systematic strategies that target rural communities and address their needs within the rural context. Rural proofing, defined as the systematic application of a rural lens across policies and guidelines to ensure that they speak to these health needs, seeks to address this gap. The healthcare professionals (HCPs) who will be called upon to advocate for and lead the implementation of rural proofing efforts are those currently in training or early career stages. We thus sought to understand the perspectives of young HCPs regarding the concept of rural proofing. METHODS: The study adopted an interpretivist paradigm. Data were collected using semi-structured individual interviews and focus group discussions (FGDs). Selected HCPs who are in leadership in Rural Seeds, a movement for young HCPs, participated in the study. FGDs in the form of Rural Cafés were led by some Rural Seeds leaders who participated in the interviews and who showed interest in organising the discussions. Eleven exploratory interviews and six FGDs were conducted using Zoom. HCPs were from Australia, Europe, Africa, North America, South America, and Asia. Interviews and FGDs were conducted in English, recorded, and transcribed verbatim. Thematic analysis was then undertaken. RESULTS: Participants perceived the state of rural healthcare globally to be problematic. Access to care was seen as the most significant issue in rural health care, associated with the challenges of lack of equity in access, and limited funding and support for healthcare professionals and their career pathways. Despite varying understanding of the concept, rural proofing was seen to be of great value in improving rural health care. A number of ideas for applying rural proofing, with examples, were proposed from their perspectives as frontline healthcare providers. They particularly recognised the importance of addressing the local needs of rural communities and the needs of present and future HCPs. Implementation of rural proofing was seen to require the involvement of key stakeholders from a range of sectors at multiple levels. CONCLUSION: Given the state of rural health, young rural HCPs suggest that rural proofing strategies are needed as they have the potential to bring about equity in the delivery of health care in rural and remote communities. These strategies will assist in creating a more positive future for rural health care worldwide and motivate young HCPs to become involved in rural health care, as well as to increase their motivation to take an interest in health policy development. These strategies need to be applied at multiple levels, from national government to local contexts. It is also seen to be critically important to involve multiple levels of stakeholders, from politicians to healthcare providers and community members, in the process of rural proofing.

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)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,328
Score d'incertitude au seuil0,998

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0030,000
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 Opus0,080
Tête enseignante GPT0,408
Écart entre enseignants0,328 · 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

Citations2
Publié2023
Routes d'admission2
Résumé présentoui

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