Exploring the long-term sustainability of a nursing best practice guidelines program
Notice bibliographique
Résumé
BACKGROUNDDespite advances in knowledge about the implementation of healthcare innovations, little attention has been focused on what happens following the early stages of change. Yet many innovations are not sustained, wasting valuable initial investments and gains. Knowledge gaps related to the sustainability of healthcare innovations are pronounced in nursing, where there is a need to determine how to heighten the "staying power" of practice improvement initiatives.PURPOSEThe purpose of this study was to understand how a nursing best practice guidelines (BPG) program was sustained over a long-term period in an acute healthcare centre.METHODOLOGYI began by conducting a concept analysis of healthcare innovation sustainability. This concept analysis provided a framework to guide a qualitative descriptive case study of an organization-wide nursing BPG program eight years following initial implementation. The case study setting was a tertiary / quaternary urban healthcare centre in Canada. The BPG program was established to improve nursing care practices related to the patient safety challenges of falls, pressure ulcers, and pain. I investigated program sustainability at the organization (nursing department) level, and then across two pairs of embedded, contrasting subcases on inpatient units in the organization. Data sources included 39 key informant interviews (14 organizational, 25 subcase), site visits, and program-related documents. FINDINGSOrganization-level and unit-level findings supported the proposed framework by providing evidence for three characteristics of sustainability (benefits, routinization / institutionalization, and development) and four categories of influencing factors (innovation, context, leadership, and process). At both levels, the combination of the three characteristics was essential; and development of the program and / or its context was increasingly important for program survival over time. Key factors influencing sustainability at the organization level were: commitment of several nursing leaders, complementarity of leadership actions, and leaders’ use of a reflection-and-course-correction strategy. Key influencing factors at the unit level were: perceptions of advantages of BPGs, collaboration, accountability, stability of staffing, linked levels of leadership, attributes of unit leadership teams, and leaders’ strategic use of activities. At both levels, the relationships between characteristics and factors accounted for how the program was sustained.CONCLUSIONSThe persistent, responsive, and complementary efforts of formal leaders, from executive to frontline roles, seem necessary for program longevity. These include strategically-aligned leadership actions focused on the dynamics between teamwork, evaluation, and learning. Such sustainability work appears to be most successful if undertaken as part of managing overall performance. Leaders should consider a broad conceptualization of sustainability that extends beyond program-related benefits and routinization / institutionalization, because development could ensure endurance. Building on the initial framework, theoretical representations of key relationships between sustainability characteristics and factors are provided. These representations can serve to guide research about and to plan for the longer-term sustainability of practice improvement initiatives.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,033 | 0,086 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,004 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), 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 ».