The Nurses' Experience Using Non-pharmacological Interventions for Persons Living with Dementia in Rural Long-Term Care Facilities in Saskatchewan
Notice bibliographique
Résumé
Introduction: Managing responsive behaviour (RB) is one of the major difficulties when caring for persons with dementia. Nearly 70% of residents in long-term care facilities (LTCF) have diagnoses of dementia. Evidence recommends nonpharmacological interventions (NPIs) as the first line of defense to treat RB, yet indicates it is not always being used. Nurses working in LTCF have direct impact on decisions of care, including interventions for RB. Minimal research exists including nurses' use of NPIs. \nPurpose: The purpose of this study was to discover the nurse experience using NPIs for persons living with dementia in rural LTCF. With no current evidence describing the nurse experience, this study seeks to fill the current knowledge gap and discover factors involved in the use of NPIs within this context.\nMethodology: Qualitative description was used with conventional content analysis. Data were collected from seven participants using semi structured interviews throughout 2019. NVivo© software was used for organizing initial categories, followed by traditional hand coding for the remainder of the analysis.\nFindings: Four major categories emerged including Sentiment and prelude to using PIs and NPIs; How and what NPIs are being used; Barriers to implementing NPIs; and Other Important Factors. Each category had several subcategories. Nurses used distraction and redirection as NPIs to manage RB in their everyday practice. While other NPIs were described (e.g., music)), they were not ordered or scheduled in any routine or systematic way. Barriers to using NPIs was a major finding. A lack of staff, time, and supportive resources were mentioned by all participants, as well as descriptions of environmental barriers including physical layout and function and organizational barriers (e.g., staffing level; policies; education, prioritizing tasks). An overall theme of struggle and complexity wove throughout all categories. \nImplications: Numerous implications for future research and LTCF organizations were found in the data including the importance of addressing barriers to the implementation of best practice using NPIs \nConclusion: The findings from this study support the need for further research to address the struggle and complexity described by nurses within LTCF when wanting to implement best practices and support those living with dementia in rural LTCF.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 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 tête enseignante, pas un consensus.
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