412 - An Affect Education Model for Caregivers: Managing Behavioural and Psychological Symptoms of Dementia
Bibliographic record
Abstract
Introduction: Providing care for people with behavioural and psychological symptoms of dementia is stressful as these individuals are commonly labelled as aggressive or resistant to care. Few studies have evaluated the impact of providing support to professional caregivers working in long-term care. Our mixed methods pilot study evaluated the impact of the innovative Affect Education Model among health care providers from two Toronto nursing homes. Methods: The two-person centred Affect Educational Model through the use of seven questions that encourage self-reflection teaches that problematic behaviours are co-constructed between individuals with BPSD and caregivers. Study procedures included recruiting nursing staff and personal support workers and teaching them the model in five weekly 30-minute group sessions. Qualitative measures in the form of focus groups were obtained. Quantitative measures were obtained through the use of five questionnaires. Results: Qualitative findings from focus groups identified four themes: facilitators and barriers perceived in current care delivery, the impact of the model experience on staff care delivery, reflections on being taught the model, and future model implementation. Quantitative results were also collected and discussed. Conclusions: The use of both pharmacologic and nonpharmacologic treatment of individuals with BPSD may be greatly enhanced by an interpersonal two-person Affect Education Model that emphasizes the importance of calming down and self-reflection. Future directions include expanding the model to family caregivers through the use of multimedia resources.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".