Research utilization in nursing care : concepts, indicators and measurements
Notice bibliographique
Résumé
Background and aim: To use research findings in patient care is a cornerstone in evidence-based practice. How and to what extent research findings are used in practice is studied in the field of research utilization (RU). The literature indicates that RU is a multifaceted and complex phenomenon and a deeper understanding of the concept in a nursing context is needed. The overall aim of this thesis was to clarify the RU concept, including instrumental RU (IRU), conceptual RU (CRU) and persuasive RU (PRU), and thus contribute to the development of better measures. Methods: Study I and II was carried out using a qualitative design with explorative (n=18) and confirming (n=3) focus groups in Sweden (n=9) and Canada (n=9+3). The convenience sample consisted of non-direct nursing care providers (n=55) and direct nursing care providers (n=74). The participants were asked to discuss different aspects of the concept(s) of RU and to propose indicators of IRU and CRU. In study III an explanative mixed methods design was used to investigate the demarcation of IRU, CRU and PRU, using 12 items proposed to measure these constructs. The items were presented to two samples: one of practicing registered nurses (n=890, target population) from a cohort in the national “Longitudinal Analyses of Nursing Education” study and one of RU experts (n=7). Qualitative content analysis was used (I, II, III) as well as various statistical analyses (III). Results: The nursing care providers did not commonly use the term research utilization and among the Swedish participants a risk for misconception of the concept was identified. Although the IRU and CRU concepts were new to the participants several examples of RU were provided; a majority of these examples related to IRU and became increasingly concrete moving from non-direct to direct care providers. RU was also discussed as a process where IRU and CRU occur on a continuum rather than as separate ways of use. IRU was described as a form of use that could occur based on direction and without awareness of the knowledge base. The most common example of IRU was to work in accordance to research-based guidelines. IRU demonstrated an acceptable demarcation in relation to CRU and PRU. CRU was described as learning or problem solving through reflective and critical thinking and was exemplified with changing attitudes or beliefs. Clinical nurses did not distinguish between CRU and PRU while RU experts did. Further the PRU items as well as the IRU items showed convergent and divergent validity compared to a golden standard, which the CRU items not did. From the proposals in focus groups several indicators of IRU and CRU were identified and from these indicators a measurement schematic was derived. Conclusions: The findings constitute new knowledge about the RU concept(s) in a nursing context, and shows differences in how RU can be understood by nurses in clinical practice and experts within the field. Based on the findings a proposal on how to conceptualize RU in the Swedish language is offered. The thesis highlights a difficulty in finding a sharp demarcation between CRU and PRU in clinical nursing. This overlap is probably related to conceptual incoherence, underlining a need for further studies. Particularly the identified indicators can be useful in improving existing or developing new measures of RU. A more valid measure of RU could be used to enhance the evaluation of interventions to support the implementation of evidence-based practice. List of scientific papers I. Estabrooks CA., Squires JE., Strandberg E., Nilsson-Kajermo K., Scott SD., Profetto-McGrath J., Harley D. & Wallin L. Towards better measures of research utilization: a collaborative study in Canada and Sweden. J Adv Nurs. 2011;67(8):1705-18. https://doi.org/10.1111/j.1365-2648.2011.05610.x II. Strandberg E, Kajermo KN, Wallin L. Vet vi vad vi talar om? Forskningsanvändning inom omvårdnad. Vård i Norden. 2010;30(4):20-25. https://doi.org/10.1177/010740831003000405 III. Strandberg E., Eldh AC., Forsman H., Rudman A., Gustavsson P. & Wallin L. Making sense of the concept of research utilization; What is what - instrumental, conceptual and persuasive research utilization? [Manuscript]
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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 | 0,042 | 0,078 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,012 | 0,015 |
| Études des sciences et des technologies | 0,001 | 0,004 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».