3. Learning Modules in a Support Group Context for Caregivers of Individuals with Alzheimer’s Disease and Related Dementias
Bibliographic record
Abstract
The majority of community dwelling individuals with dementia have informal caregivers. With the risk of developing care related stress, it is important for community agencies, like the Alzheimer Society of Kingston (ASK), to provide support, resources, and information. The goal of this intervention was that caregivers of individuals with Alzheimer’s disease and related dementias (ADRD) would improve physical, social, and mental well-being. A search of the literature, as well as surveys of support group facilitators, forty caregivers, and consultation with ASK helped determine information relevant to the caregivers. The purpose of this project was to develop learning modules on: a) using a problem-based strategy to assist with daily activities of the person they are caring for; b) self-care strategies; c) positive coping strategies; d) assertive communication skills, and e) home environment safety to be delivered during caregiver support groups. The module on a problem-based strategy to assist with daily activities was pilot tested, utilizing five caregivers. It was found that the learning module was approximately 30 minutes over the stated timeframe and the case study component of the module was ineffective. Recommendations include implementing the module as two parts. Furthermore, additional research is needed on the impact of the modules on caregiver stress and burden.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".