An in-depth qualitative study of health care providers’ experiences of performance-based financing program as a nation-wide adopted policy in Cameroon: A principal-agent perspective
Notice bibliographique
Résumé
OBJECTIVES: The study applies the principal-agent approach to explore providers' experiences before and after the introduction of performance-based financing (PBF) in Cameroon, challenges and facilitators in the implementation process, and mechanisms in place to ensure sustainability. METHODS: The study was an in-depth qualitative study whose goal was to provide multiple descriptions of experiences and insights from a principal-agent analysis perspective. Purposive sampling was used to identify the key characteristics of the participants relevant to the study. A snowballing technique was used to further identify eligible participants. Only healthcare providers who were exposed to the previous system and could reflect on and provide meaningful data that captured the everyday experiences before and after the implementation of PBF were included. Data were collected from three districts in the Southwest region of Cameroon from May 2021 to August 2021. Data were transcribed and analyzed using MaxQDA. RESULTS: A total of 17 interviews and 3 focus group discussions (24 participants) were conducted with healthcare providers and key stakeholders involved in PBF. The respondents described a range of changes that they had experienced since the introduction of PBF. Each of these changes was categorized as either positive or negative. Positive changes were framed into 14 dominant categories: motivation, negotiations, innovation, resource allocation, autonomy, decentralization, transparency, improved quality of care, separation of function, performance, equity considerations, opportunity to recruit, participation in decision-making, and improved access to and utilization of maternal health services. The main challenges (negative experiences) reported were framed into nine categories: management of change, retention issues, conflict of interest, poor understanding of the PBF concept, resistance to change, verification challenges, delays in payment of PBF incentives, data entry and documentation, and challenges in meeting the equity considerations of the poor and vulnerable. Despite the challenges, providers preferred the decentralized approach to the centralized system. CONCLUSION: PBF is a national strategy for achieving universal health coverage in Cameroon, and the experiences of providers provide a vital guide to refine national policy. The introduction of PBF has provided positive changes to providers' quality of care when compared to the previous system. Addressing the delays in PBF payments will help to overcome the challenges to implementation and provide opportunities for health facilities to be more efficient and improve their performance. Despite the limitations of delay in payment, PBF helps to align the incentives of the health workers (agent) with those of the Ministry of Health (principal).
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Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,009 | 0,008 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».