The Nurses' Experience Using Non-pharmacological Interventions for Persons Living with Dementia in Rural Long-Term Care Facilities in Saskatchewan
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".