The emergence of family medicine in India–A qualitative descriptive study
Notice bibliographique
Résumé
Countries globally are introducing family medicine to strengthen primary health care; however, for many, that process has been slow. Understanding the implementation of family medicine in a national context is complex but critical to uncovering what worked, the challenges faced, and how the process can be improved. This study explores how family medicine was implemented in India and how early cohort family physicians supported the field's emergence. In this qualitative descriptive study, we interviewed twenty family physicians who were among the first in India and recognized as pioneers. We used Rogers's Diffusion of Innovation Theory to describe and understand the roles of family physicians, as innovators and early adopters, in the process of implementation. Greenhalgh's Model of Diffusion in Service Organizations is applied to identify barriers and enablers to family medicine implementation. This research identifies multiple mechanisms by which pioneering family physicians supported the implementation of family medicine in India. They were innovators who developed the first family medicine training programs. They were early adopters willing to enter a new field and support spread as educators and mentors for future cohorts of family physicians. They were champions who developed professional organizations to bring together family physicians to learn from one another. They were advocates who pushed the medical community, governments, and policymakers to recognize family medicine's role in healthcare. Facilitators for implementation included the supportive environment of academic institutions and the development of family medicine professional organizations. Barriers to implementation included the lack of government support and awareness of the field by society, and tension with subspecialties. In India, the implementation of family medicine has primarily occurred through pioneering family physicians and supportive educational institutions. For family medicine to continue to grow and have the intended impacts on primary care, government and policymaker support are needed.
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Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 tête enseignante, pas un consensus.
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