Human resource for health reform in peri-urban areas: a cross-sectional study of the impact of policy interventions on healthcare workers in Epworth, Zimbabwe
Notice bibliographique
Résumé
BACKGROUND: The need to understand how healthcare worker reform policy interventions impact health personnel in peri-urban areas is important as it also contributes towards setting of priorities in pursuing the universal health coverage goal of health sector reform. This study explored the impact of post 2008 human resource for health reform policy interventions on healthcare workers in Epworth, a peri-urban community in Harare, Zimbabwe, and the implications towards health sector reform policy in peri-urban areas. METHODS: The study design was exploratory and cross-sectional and involved the use of qualitative and quantitative methods in data collection, presentation, and analysis. A qualitative study in which data were collected through a documentary search, five key informant interviews, seven in-depth interviews, and five focus group discussions was carried out first. This was followed by a quantitative study in which data were collected through a documentary search and 87 semi-structured sample interviews with healthcare workers. Qualitative data were analyzed thematically whilst descriptive statistics were used to examine quantitative data. All data were integrated during analysis to ensure comprehensive, reliable, and valid analysis of the dataset. RESULTS: Three main factors were identified to help interpret findings. The first main factor consisted policy result areas that impacted most successfully on healthcare workers. These included the deployment of community health workers with the highest correlation of 0.83. Policy result areas in the second main factor included financial incentives with a correlation of 0.79, training and development (0.77), deployment (0.77), and non-financial incentives (0.75). The third factor consisted policy result areas that had the lowest satisfaction amongst healthcare workers in Epworth. These included safety (0.72), equipment and tools of trade (0.72), health welfare (0.65), and salaries (0.55). CONCLUSIONS: The deployment of community health volunteers impacted healthcare workers most successfully. This was followed by salary top-up allowances, training, deployment, and non-financial incentives. However, health personnel were least satisfied with their salaries. This had negative implications towards health sector reform interventions in Epworth peri-urban community between 2009 and 2014.
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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 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| 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 | 0,000 |
| 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 ».