Physicians’ perceptions on the impact of telemedicine on recruitment and retention in underserved areas: a descriptive study in Senegal
Notice bibliographique
Résumé
BACKGROUND: Similar to many places, physicians in Senegal are unevenly distributed. Telemedicine is considered a potential solution to this problem. This study investigated the perceptions of Senegal's physicians of the impact of telemedicine on their recruitment to and retention in underserved areas. METHODS: We conducted individual interviews with a random sample of 60 physicians in Senegal, including 30 physicians working in public hospitals and 30 physicians working in district health centres between January and June 2014, as part of a mixed methods study. Data were collected using a semi-structured interview guide comprising both open- and close-ended questions. Interviews were recorded, transcribed and coded thematically using NVivo 10 software using a priori and emergent codes. Participants' characteristics were analyzed descriptively using SPSS 23. RESULTS: The impact of telemedicine on physicians' recruitment and retention in underserved areas was perceived with some variability. Among the physicians who were interviewed, most (36) thought that telemedicine could have a positive impact on their recruitment and retention but many (24) believed the opposite. The advantages noted by the first included telemedicine's ability to break their professional isolation and reduce the stress related to this, facilitate their distance learning and improve their working conditions. They did acknowledge that it is not sufficient in itself, an opinion also shared by physicians who did not believe that telemedicine could affect their recruitment and retention. Both identified contextual, economic, educational, family, individual, organizational and professional factors as influential. CONCLUSION: Based on these opinions of physicians, telemedicine promotion is one intervention that, alongside others, could be promoted to assist in addressing the multiple factors that influence physicians' recruitment and retention in underserved areas.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».