Comparability of family planning quality of care measurement tools in low-and-middle income country settings: a systematic review
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Notice bibliographique
Résumé
BACKGROUND: In low-and-middle income countries (LMICs), accurate measures of the elements of quality care provided by a health worker through family planning services (also known as process quality) are required to ensure family's contraceptives needs are being met. There are many tools used to assess family planning process quality of care (QoC) but no one standardized method. Those measuring QoC in LMICs should select an appropriate tool based the program context and financial/logistical parameters, but they require data on how well each tool measures routine clinical care. We aim to synthesize the literature on validity/comparability of family planning process QoC measurement tools through a quantitative systematic review with no meta-analysis. METHODS: We searched six literature databases for studies that compared quality measurements from different tools using quantitative statistics such as sensitivity/specificity, kappa statistic or absolute difference. We extracted the comparative measure along with other relevant study information, organized by quality indicator domain (e.g. counseling and privacy), and then classified the measure by low, medium, and high agreement. RESULTS: We screened 8172 articles and identified eight for analysis. Studies comparing quality measurements from simulated clients, direct observation, client exit interview, provider knowledge quizzes, and medical record review were included. These eight studies were heterogenous in their methods and the measurements compared. There was insufficient data to estimate overall summary measures of validity for the tools. Client exit interviews compared to direct observation or simulated client protocols had the most data and they were a poor proxy of the actual quality care received for many measurements. CONCLUSION: To measure QoC consistently and accurately in LMICs, standardized tools and measures are needed along with an established method of combining them for a comprehensive picture of quality care. Data on how different tools proxy quality client care will inform these guidelines. Despite the small number of studies found during the review, we described important differences on how tools measure quality of care.
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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 | 0,008 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,010 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 | 0,001 |
| 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 découle