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Enregistrement W2769344251 · doi:10.22605/rrh4285

Understanding how emergency medicine physicians survive and thrive in rural practice: a theoretical model

2017· article· en· W2769344251 sur OpenAlexafffundabout
Ashra Kolhatkar, Andrea Keesey, Bob Bluman, Brenna M. Lynn, Tandi Wilkinson

Notice bibliographique

RevueRural and Remote Health · 2017
Typearticle
Langueen
DomaineHealth Professions
ThématiqueGlobal Health Workforce Issues
Établissements canadiensInstitute of Health Services and Policy ResearchUniversity of British Columbia Hospital
Organismes subventionnairesUniversity of British ColumbiaDoctors of BC
Mots-clésMedicineFailure to thriveFamily medicinePsychologyMedical educationNursingPediatrics

Résumé

récupéré en direct d'OpenAlex

INTRODUCTION: The challenges facing emergency medicine (EM) services in Canada reflect the limitations of the entire healthcare system. The emergency department (ED) is uniquely situated in the healthcare system such that shortcomings in hospital- and community-based services are often first revealed there. This is especially true in rural settings, where there are additional site-specific barriers to the provision of EM care. Existing studies look at the factors that influence rural EM physicians in isolation. This study uses a qualitative approach and generates a theoretical model that describes the complex interplay between major factors that influence the experience of rural EM physicians. METHODS: Eight focus groups were conducted with 39 physicians from rural British Columbia, Canada. Semi-structured focus group protocols were designed to leverage the diversity of the focus groups, which included rural generalists, full-time EM practitioners, physicians from very small and remote communities, locums, international medical graduates, physicians new to practice, and physicians who no longer practice rural EM. Following the principles of grounded theory, interview probes were adjusted iteratively to reflect emerging findings. Transcripts were analysed to identify codes and major themes, which served as the basis for the theoretical model. RESULTS: The theoretical model reveals how the causal conditions (a lack of medical and human resources, and the isolation of rural communities due to topography, distance, and inclement weather) contribute to physicians' common experience of feeling fearful and under-supported at work. Two core phenomena emerge as important needs: supportive professional relationships, and healthcare system adaptability. Contextual factors such as remuneration and continuing medical education funding, and the intervening conditions of physicians' rural exposure during formative years, also have an effect. Physicians create innovative solutions to address the challenges that arise in the practice of rural EM. Ultimately, the ability to manage the pressures of rural EM leads physicians to either thrive in or leave rural EM practice. CONCLUSIONS: The theoretical model provides a more complex view of the realities of rural EM care than has been previously described. It identifies factors that enable and hinder rural EM physicians in their practice, and provides an understanding of the strategies they employ to navigate challenges. Some elements of the theoretical model have been previously identified. For example, existing work has found that many rural physicians experience fear and anxiety in their practice. The challenges posed by the variation in rural practice environments have also been previously identified as an important influence. Other elements of the theoretical model, and the common need for practitioners to creatively respond to barriers arising from the healthcare system's inability to respond to local needs, have not been previously identified. This work finds these factors to be a common experience for participants, and as such, more widespread recognition of the importance of these factors could lead to system improvements. Future research is needed to test the hypotheses proposed in this study and explore the generalizability of the findings.

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 candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: Théorique ou conceptuel
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,583
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0030,001
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 Opus0,136
Tête enseignante GPT0,473
Écart entre enseignants0,337 · 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'étudeThéorique ou conceptuel
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

Citations11
Publié2017
Routes d'admission3
Résumé présentoui

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