Strategic analysis of interventions to reduce physician shortages in rural regions
Notice bibliographique
Résumé
INTRODUCTION: Physician shortages in rural regions of OECD countries has led to the development of regulatory, financial, educational and tailored interventions designed to reduce physician shortages. Studies evaluating these interventions report weak or inconclusive results. The objective of this research is to examine the strategic relevance of the interventions by identifying and prioritizing the determinants of physician shortages and analyzing the interventions based on their ability to target the determinants. METHODS: First, the determinants of physician shortages were identified and categorized using Mays et al's 2005 method for reviewing qualitative literature. Second, the determinants were prioritized based on importance, severity and solvability, using Lehmann et al's multilevel categorization of factors affecting attraction and retention. Third, the interventions were analyzed based on their ability to target the determinants through a document analysis as descriptive commentary from a policy analysis perspective. RESULTS: Three individual and 10 contextual (work, rural or international context) determinants of physician shortages were identified. Non-rural background, inadequate training and inadequate incentive structure were prioritized as level 1. Lack of professional support, poor work infrastructure and personal interests were prioritized as level 2. Poor rural infrastructure, inadequate supply planning and cultural difference were prioritized as level 3. Non-minority background, geography and climate, global migration and aging population were prioritized as level 4. Establishing rural medical schools targets the greatest number of priority determinants, followed by financial interventions targeting practicing physicians and non-traditional health services delivery strategies. Curriculum changes, professional support strategies, selective admission to medical schools, financially targeting student physicians and coercive regulatory measures follow. Community support strategies target the fewest number of determinants and trickle-down economic regulation targets none. CONCLUSION: Strategic analysis demonstrates that most interventions designed to reduce physician shortages in rural regions are strategically relevant because they address the priority determinants of physician shortages. A link is established between the determinants of physician shortages and the interventions, thereby addressing an important concern expressed in the literature. An original contribution is made to health human resources literature by relying on established theoretical frameworks to achieve a strategic analysis of the interventions.
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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 | 0,008 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 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 ».