Trialists perspectives on sustaining, spreading, and scaling-up of quality improvement interventions
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Notice bibliographique
Résumé
BACKGROUND: Quality improvement (QI) evaluations rarely consider how a successful intervention can be sustained long term, nor how to spread or scale to other locations. A survey of authors of randomized trials of diabetes QI interventions included in an ongoing systematic review found that 78% of trials reported improved quality of care, but 40% of these trials were not sustained. This study explores why and how the effective interventions were sustained, spread, or scaled. METHODS: A qualitative approach was used, focusing on case examples. Diabetes QI program trial authors were purposefully sampled and recruited for telephone interviews. Authors were eligible if they had completed the author survey, agreed to follow-up, and had a completed a diabetes QI trial they deemed "effective." Snowball sampling was used if the participant identified someone who could provide a different perspective on the same trial. Interviews were transcribed verbatim. Inductive thematic analysis was conducted to identify barriers and facilitators to sustainability, spread, and/or scale of the QI program, using case examples to show trajectories across projects and people. RESULTS: Eleven of 44 eligible trialists participated in an interview. Four reported that the intervention was "sustained" and nine were "spread," however, interviews highlighted that these terms were interpreted differently over time and between participants. Participant stories highlighted the varied trajectories of how projects evolved and how some research careers adapted to increase impact. Three interacting themes, termed the "3C's," helped explain the variation in sustainability, spread, and scale: (i) understanding the concepts of implementation, sustainability, sustainment, spread, and scale; (ii) having the appropriate competencies; and (iii) the need for individual, organizational, and system capacity. CONCLUSIONS: Challenges in defining sustainability, spread and scale make it difficult to fully understand impact. However, it is clear that from the beginning of intervention design, trialists need to understand the concepts and have the competency and capacity to plan for feasible and sustainable interventions that have potential to be sustained, spread and/or scaled if found to be effective.
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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 | 0,009 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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