GENTLE PERSUASIVE APPROACHES IN DEMENTIA CARE: BUILDING STAFF CONFIDENCE AND EFFICACY
Bibliographic record
Abstract
Literature suggests that patients living with dementia in the hospital are complex to care for, and staff are underprepared to meet their unique needs. This proportion is expected to significantly increase, creating an urgent call to action. The Gentle Persuasive Approach (GPA) in Dementia Care: Supporting Persons with Responsive Behaviours is an evidence-based Canadian curriculum that is designed to help provide person-centered care for patients living with dementia in residential care, and offers a potential solution for other areas of practice. This poster highlights the benefits and limitations of introducing GPA in acute medicine and mental health units at Vancouver General Hospital. We used mixed methods to evaluate a GPA education program delivered through a 7.5-hour workshop for staff members from acute medicine and mental health units. After the GPA workshop, 112 staff completed the standardized GPA program evaluation survey. Using semi-structured open-ended questions, we interviewed 22 staff that completed the GPA education. Staff responses were thematically analysed. Eighty five percent of staff that attended the GPA workshops had no previous formal dementia care education. All staff agreed that the GPA course would improve how they cared for people with dementia in the hospital. Results of the interviews revealed useful information for future facilitation of GPA implementation in the hospital setting. Supporting the facilitators and addressing limitations around GPA implementation can further improve the confidence, efficacy and capacity for staff to successfully care for patients living with dementia in the hospital.
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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 | 0.028 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".