Predicting Research Use in Nursing Organizations
Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
BACKGROUND: No empirical literature was found that explained how organizational context (operationalized as a composite of leadership, culture, and evaluation) influences research utilization. Similarly, no work was found on the interaction of individuals and contextual factors, or the relative importance or contribution of forces at different organizational levels to either such proposed interactions or, ultimately, to research utilization. OBJECTIVE: To determine independent factors that predict research utilization among nurses, taking into account influences at individual nurse, specialty, and hospital levels. DESIGN: Cross-sectional survey data for 4,421 registered nurses in Alberta, Canada were used in a series of multilevel (three levels) modeling analyses to predict research utilization. METHODS: A multilevel model was developed in MLwiN version 2.0 and used to: (a) estimate simultaneous effects of several predictors and (b) quantify the amount of explained variance in research utilization that could be apportioned to individual, specialty, and hospital levels. FINDINGS: There was significant variation in research utilization (p <.05). Factors (remaining in the final model at statistically significant levels) found to predict more research utilization at the three levels of analysis were as follows. At the individual nurse level (Level 1): time spent on the Internet and lower levels of emotional exhaustion. At the specialty level (Level 2): facilitation, nurse-to-nurse collaboration, a higher context (i.e., of nursing culture, leadership, and evaluation), and perceived ability to control policy. At the hospital level (Level 3): only hospital size was significant in the final model. The total variance in research utilization was 1.04, and the intraclass correlations (the percent contribution by contextual factors) were 4% (variance = 0.04, p <.01) at the hospital level and 8% (variance = 0.09, p <.05) at the specialty level. The contribution attributable to individual factors alone was 87% (variance = 0.91, p <.01). CONCLUSIONS: Variation in research utilization was explained mainly by differences in individual characteristics, with specialty- and organizational-level factors contributing relatively little by comparison. Among hospital-level factors, hospital size was the only significant determinant of research utilization. Although organizational determinants explained less variance in the model, they were still statistically significant when analyzed alone. These findings suggest that investigations into mechanisms that influence research utilization must address influences at multiple levels of the organization. Such investigations will require careful attention to both methodological and interpretative challenges present when dealing with multiple units of analysis.
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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 | 0,052 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,012 |
| Études des sciences et des technologies | 0,005 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
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