Challenges of scaling up and of knowledge transfer in an action research project in Burkina Faso to exempt the worst-off from health care user fees
Notice bibliographique
Résumé
BACKGROUND: Systems to exempt the indigent from user fees have been put in place to prevent the worst-off from being excluded from health care services for lack of funds. Yet the implementation of these mechanisms is as rare as the operational research on this topic. This article analyzes an action research project aimed at finding an appropriate solution to make health care accessible to the indigent in a rural district of Burkina Faso. RESEARCH: This action research project was initiated in 2007 to study the feasibility and effectiveness of a community-based, participative and financially sustainable process for exempting the indigent from user fees. A interdisciplinary team of researchers from Burkina Faso and Canada was mobilized to document this action research project. RESULTS AND KNOWLEDGE SHARING: The action process was very well received. Indigent selection was effective and strengthened local solidarity, but coverage was reduced by the lack of local financial resources. Furthermore, the indigent have many other needs that cannot be addressed by exemption from user fees. Several knowledge transfer strategies were implemented to share research findings with residents and with local and national decision-makers. PARTNERSHIP ACHIEVEMENTS AND DIFFICULTIES: Using a mixed and interdisciplinary research approach was critical to grasping the complexity of this community-based process. The adoption of the process and the partnership with local decision-makers were very effective. Therefore, at the instigation of an NGO, four other districts in Burkina Faso and Niger reproduced this experiment. However, national decision-makers showed no interest in this action and still seem unconcerned about finding solutions that promote access to health care for the indigent. LESSONS LEARNED: The lessons learned with regard to knowledge transfer and partnerships between researchers and associated decision-makers are: i) involve potential users of the research results from the research planning stage; ii) establish an ongoing partnership between researchers and users; iii) ensure that users can participate in certain research activities; iv) use a variety of strategies to disseminate results; and v) involve users in dissemination activities.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».