Nurses' Experiences in Caring for Older Adults With Responsive Behaviors of Dementia in Acute Care
Bibliographic record
Abstract
Introduction: Approximately 56,000 individuals with dementia were admitted to Canadian hospitals in 2016, and 75% of them experience responsive behaviors. Responsive behaviors are words or actions used to express one's needs (e.g., wandering, yelling, hitting, and restlessness). Health-care professionals perceive these behaviors to be a challenging aspect in providing care for persons with dementia. Aims: This study explores the perceptions of nurses about (a) caring for older adults with dementia experiencing responsive behaviors in acute medical settings and (b) recommendations to improve dementia care. Methods: Thorne's interpretive description approach was used. In-person, semistructured interviews were conducted with 10 nurses and 5 allied health professionals from acute medical settings in an urban hospital in Ontario. Interviews were conducted with allied health professionals to understand their perspectives regarding care delivery for persons with responsive behaviors of dementia. Data were analyzed using Braun and Clarke's experiential thematic analysis. Findings: Themes related to caring for individuals with responsive behaviors included (a) delivering care is a complex experience, (b) using pharmacological strategies and low investment nonpharmacological strategies to support older adults with responsive behaviors, (c) acute medical settings conflicted with principles of dementia care due to a focus on acute care priorities and limited time, and (d) strong interprofessional collaboration and good continuity of care were facilitators for care. Conclusions: Findings provide guidance for improved support for nurses who provide care for individuals experiencing responsive behaviors in acute medical settings such as increasing staffing and providing educational reinforcements (e.g., annual review of dementia care education and in-services).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".