Spreading and sustaining best practices for home care of older adults: a grounded theory study
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Notice bibliographique
Résumé
BACKGROUND: Improving health care quality requires effective and timely spread of innovations that support evidence-based practices. However, there is limited rigorous research on the process of spread, factors influencing spread, and models of spread. It is particularly important to study spread within the home care sector given the aging of the population, expansion of home care services internationally, the high proportion of older adult users of home care services, and the vulnerability of this group who are frail and live with multiple chronic conditions. The purpose of this study was to understand how best practices related to older adults are spread within home care organizations. METHODS: Four home care organizations in Ontario, Canada that had implemented best practices related to older adults (falls prevention, pain management, management of venous leg ulcers) participated. Using a qualitative grounded theory design, interviews were conducted with frontline providers, managers, and directors at baseline (n = 44) and 1 year later (n = 40). Open, axial, and selective coding and constant comparison analysis were used. RESULTS: A model of the process of spread of best practices within home care organizations was developed. The phases of spread included (1) committing to change, (2) implementing on a small scale, (3) adapting locally, (4) spreading internally to multiple users and sites, and (5) disseminating externally. Factors that facilitated progression through these phases were (1) leading with passion and commitment, (2) sustaining strategies, and (3) seeing the benefits. Project leads, champions, managers, and steering committees played vital roles in leading the spread process. Strategies such as educating/coaching and evaluating and feedback were key to sustaining the change. Spread occurred within the home care context of high staff and manager turnover and time and resource constraints. CONCLUSIONS: Spread of best practices is optimized through the application of the phases of spread, allocation of resources to support spread, and implementing strategies for ongoing sustainability that address potential barriers. Further research will help to understand how best practices are spread externally to other organizations.
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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 | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
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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