Effect of the Organisational Development Tool Appreciative Inquiry [Internet]
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Notice bibliographique
Résumé
The Norwegian Knowledge Centre for the Health Services was commissioned by The Regional Health Authority of South-Eastern Norway, Unit for Service Development and Cooperation to summarize available research on the effect of the organisational change methodology Appreciative Inquiry (AI). The aim of this review is to answer whether AI was more effective than other organizational development methods during a process of change in an organization. Even though we wished to focus on changes in the health services, we did not restrict the outcomes, where the intervention had taken place or what kind of organisational change that was studied.We searched for controlled studies of effect both in medical and social electronic databases and identified 367 references. We included the six studies that had a control group. All were controlled before and after studies.The included studies were conducted in different enterprises, a ward in a hospital in England, US Postal Services, a chain of fast food restaurants, a manufacturer of freight elevator doors, a trucking company, all in the USA, and a group of students in Canada. Several of the studies had more than one outcome, but none had measured an outcome in the same way. The outcomes comprised absence due to sickness, turnover, attitudes toward colleagues that make mistakes, conflict management, task quality, trust in the recourses of the group and a wish for future cooperation.We assessed the studies to have an unclear or high risk of bias. The quality of documentation for effect of AI was very low, and we cannot draw clear conclusions. Some of the included studies reported that AI seemed to be more efficient than other organisational development tools, others not. Some of the included studies also reported that AI sometimes was not more efficient than not using any development tools. Future studies on the effect of AI should be larger and of better quality than the identified studies. The elements of AI that is the focus of research should be clearly specified and the outcomes more precisely defined.
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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 | 0,000 | 0,000 |
| 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 | 0,000 | 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) | 0,003 | 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