How does the effectiveness of strategies to improve healthcare provider practices in low-income and middle-income countries change after implementation? Secondary analysis of a systematic review
Notice bibliographique
Résumé
BACKGROUND: A recent systematic review evaluated the effectiveness of strategies to improve healthcare provider (HCP) performance in low-income and middle-income countries. The review identified strategies with varying effects, including in-service training, supervision and group problem-solving. However, whether their effectiveness changed over time remained unclear. In particular, understanding whether effects decay over time is crucial to improve sustainability. METHODS: We conducted a secondary analysis of data from the aforementioned review to explore associations between time and effectiveness. We calculated effect sizes (defined as percentage-point (%-point) changes) for HCP practice outcomes (eg, percentage of patients correctly treated) at each follow-up time point after the strategy was implemented. We estimated the association between time and effectiveness using random-intercept linear regression models with time-specific effect sizes clustered within studies and adjusted for baseline performance. RESULTS: The primary analysis included 37 studies, and a sensitivity analysis included 77 additional studies. For training, every additional month of follow-up was associated with a 0.19 %-point decrease in effectiveness (95% CI: -0.36 to -0.03). For training combined with supervision, every additional month was associated with a 0.40 %-point decrease in effectiveness (95% CI: -0.68 to -0.12). Time trend results for supervision were inconclusive. For group problem-solving alone, time was positively associated with effectiveness, with a 0.50 %-point increase in effect per month (95% CI: 0.37 to 0.64). Group problem-solving combined with training was associated with large improvements, and its effect was not associated with time. CONCLUSIONS: Time trends in the effectiveness of different strategies to improve HCP practices vary among strategies. Programmes relying solely on in-service training might need periodical refresher training or, better still, consider combining training with group problem-solving. Although more high-quality research is needed, these results, which are important for decision-makers as they choose which strategies to use, underscore the utility of studies with multiple post-implementation measurements so sustainability of the impact on HCP practices can be assessed.
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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 | 0,048 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,007 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».