Interventions to Improve the Nursing Care of People with Dementia in Canadian Hospitals: An Environmental Scan
Bibliographic record
Abstract
As the number of people with dementia admitted to hospitals is expected to grow, now is the time to identify methods to improve nursing care of this population. We conducted an environmental scan to identify and describe interventions in Canadian hospitals to improve the nursing care of people with dementia, how they are being evaluated and what issues influence the success of interventions. Methods included a search of published and unpublished literature and key stakeholder interviews. Interventions are described under three categories: (1) interventions to improve nurses' knowledge, attitudes and skills; (2) interventions to address responsive behaviours; and (3) interventions to help nurses individualize care. The evaluation of interventions rarely included an evaluation of effectiveness and more often included a qualitative evaluation of nurses' experiences with interventions. We summarize the factors affecting the implementation of interventions following the Consolidated Framework for Implementation Research (Damschroder et al. 2009) and suggest strategies for supporting the success of interventions to improve patient care and the experiences of nurses working with people with dementia.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.027 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".