Shaping the future rural healthcare landscape: perspectives of young healthcare professionals
Notice bibliographique
Résumé
INTRODUCTION: Rural communities continue to struggle to access quality healthcare services. Even in countries where the majority of the population live in rural and remote areas, resources are concentrated in big cities, and this is continuing. As a result, countries with the highest proportion of rural residents correlate with the poorest access, which has negative implications for the health and wellbeing of people. Healthcare professionals (HCPs) have been identified as key informants in the construction and implementation of policies aimed at addressing rural health issues. We sought to understand the perspectives of young HCPs, representing the potential future rural workforce, regarding the future of rural health care. METHODS: An interpretivist paradigm was adopted for the study. Data were collected in two phases over Zoom using semi-structured individual interviews and focus group discussions (FGDs). Participants included selected HCPs who are members of Rural Seeds, which is a global movement for young HCPs. A total of 11 exploratory interviews and six FGDs were conducted. The 11 interviewees consisted of medical doctors and medical students from 10 countries classified at different levels of development by the WHO. The six FGDs ranged from three to nine participants, and they included medical doctors and medical students, nurses and rehabilitation therapists. Participants came from South Asia, Africa, Asia-Pacific, North America and Europe, and South America. Both interviews and FGDs were conducted in English, recorded, and transcribed verbatim. Data were analysed utilising thematic analysis. RESULTS: Similar themes were identified across both individual interviews and FGDs. The state of rural health care was perceived to be problematic by all the participants. Access to care, lack of equity and multiple socioeconomic challenges, particularly in relation to living conditions, human resources and infrastructure, were seen as the most significant issues in rural health care. Several ideas for addressing rural health issues, with examples, were proposed by the young HCPs from their perspectives as frontline healthcare providers. They particularly recognised the importance of addressing the local socioeconomic and developmental needs of rural communities, and the needs of present and future HCPs. CONCLUSION: Young health professionals from across the world interested in a rural career have common concerns about the state of rural health in their countries and constructive insights into how these can be addressed. They suggest effective solutions that must include listening to their voices. This article is a step in that direction.
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Comment cette classification a été obtenuedéplier
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 | 0,009 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,012 | 0,008 |
| Communication savante | 0,009 | 0,005 |
| Science ouverte | 0,001 | 0,007 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».