THE STAFF EXPERIENCES OF THE IMPLEMENTATION OF GENTLE PERSUASIVE APPROACHES
Bibliographic record
Abstract
Because dementia care is often considered challenging, the importance of offering dementia-specific education has been emphasized. The Gentle Persuasive Approaches (GPA) programme was designed to enhance staff members’ competency and knowledge in dementia care through person-centered strategy training. The GPA was implemented in Vancouver General Hospital, a large urban hospital in British Columbia, Canada since 2014. A total of 310 staff in multiple disciplines were trained. The post-training survey (N= 297) showed highly positive results. Almost all of the staff (99 %) reported finding the training useful and applicable. Two focus groups with 20 staff (physicians, nurses, allied health professionals, care staff) were conducted to explore the impact of their learning experience, and what affected them to apply new knowledge in practice. Our analysis revealed three themes: (1) the program enables staff to change practices by focusing on the values of person-centered care, (2) the program promotes teamwork with interdisciplinary healthcare professionals, patients and families, and (3) barriers to applying the new practice are management and organizational support. The staff reported that the programme allows them to consider patients’ preferences and respect their boundaries, empathize with patients’ experiences, become proactive in making changes in dementia care and work collaboratively with the team. However, these positive outcomes are hindered by workload, staffing, lack of managerial support and the complexity of care. In this presentation, we will discuss the implications of the GPA programme on staff practice and offer practical strategies to sustaining the programme’s effectiveness in acute care.
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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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".