A scoping review of the use of quality improvement methods by community organizations in the United States, Australia, New Zealand, and Canada to improve health and well-being in community settings
Notice bibliographique
Résumé
Abstract Background Health-care facilities have used quality improvement (QI) methods extensively to improve quality of care. However, addressing complex public health issues such as coronavirus disease 2019 and their underlying structural determinants requires community-level innovations beyond health care. Building community organizations’ capacity to use QI methods is a promising approach to improving community health and well-being. Objectives We explore how community health improvement has been defined in the literature, the extent to which community organizations have knowledge and skill in QI and how communities have used QI to drive community-level improvements. Methods Per a published study protocol, we searched Scopus, Web of Science, and Proquest Health management for articles between 2000 and 2019 from USA, Australia, New Zealand, and Canada. We included articles describing any QI intervention in a community setting to improve community well-being. We screened, extracted, and synthesized data. We performed a quantitative tabulation and a thematic analysis to summarize results. Results Thirty-two articles met inclusion criteria, with 31 set in the USA. QI approaches at the community level were the same as those used in clinical settings, and many involved multifaceted interventions targeting chronic disease management or health promotion, especially among minority and low-income communities. There was little discussion on how well these methods worked in community settings or whether they required adaptations for use by community organizations. Moreover, decision-making authority over project design and implementation was typically vested in organizations outside the community and did not contribute to strengthening the capability of community organizations to undertake QI independently. Conclusion Most QI initiatives undertaken in communities are extensions of projects in health-care settings and are not led by community residents. There is urgent need for additional research on whether community organizations can use these methods independently to tackle complex public health problems that extend beyond health-care quality.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,014 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,004 |
| 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 ».