Training leads to improved performance of Health Unit Management Committees in south western Uganda manuscript
Notice bibliographique
Résumé
Abstract Background: A quality health workforce is critical for the development of health systems and effective delivery of health services. In southwestern Uganda, Health Unit Management Committees (HUMCs) are central to the delivery of health care. They also play a key role in facilitating links between health centres and the community, as they comprised of community members. While these teams took part in planning and management training between 2012-2015, no analysis had been done with regards to the outcomes of these training. This study sought, therefore, to determine whether HUMC members saw increased performance outcomes as a result of their training. Methods: The study followed a cross sectional evaluation design and adopted qualitative methods, including Focus Group Discussions (FGDs), Key Informant Interviews (KIIs) and In-Depth Interviews with health unit In-charges (managers), district health team members and project intervention staff. Evaluation was conducted in July 2016 in Bushenyi district in southwestern Uganda. Evaluation was completed in all levels of health care centers and in both urban and rural settings. Data was collected by members of the research team in both Runyankole and English, and translated into English. Results: Findings revealed that HUMCs reported to be more capable of handling issues at the facility as a result of knowledge and skills acquired during trainings. HUMCs identified several key learning themes, including: conflict resolution, strengthened relationships between members and increased community engagement. The training also resulted in several initiatives for increased health care outcomes, including saving schemes for emergency transportation of referrals, construction of placenta pit and canteen, and beautification projects. Overall there were positive feelings towards the training and its relevance for HUMCs’ job performance. Discussion: In examining the results of the study, conclusions can be drawn that training for HUMCs, which had been the first of their kind in this area, increased performance outcomes in health centers. This aligns with similar research, which identified management training for health care management teams as an important factor for improving the delivery of health services.
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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 | 0,005 | 0,026 |
| 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 | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
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 ».