Effectiveness of Communication Strategies Used by Caregivers of Persons With Alzheimer's Disease During Activities of Daily Living
Bibliographic record
Abstract
Communication difficulties between individuals with Alzheimer's disease (AD) and their caregivers are commonly reported. Caregivers carry the burden of managing breakdowns in communication because people with AD are often unable to modify their communicative behavior. To assist caregivers in this endeavor, clinicians and caregiving professionals have offered a variety of strategies aimed at accommodating the individual's declining abilities. Many of these strategies are intuitively appealing, but they lack empirical support. This study investigated the effectiveness of 10 frequently recommended communication strategies when employed by family caregivers of persons with AD. In particular, we assessed (a) which strategies family caregivers report using and with what degree of success, (b) which of these strategies are used by caregivers in actual interactions with their spouses, and (c) which strategies contribute to improved communication. The study included a self-report questionnaire and wireless audio-recorded interactions between 18 persons with AD and their spousal caregivers during activities of daily living. The findings validate the effectiveness of certain communication strategies (e.g., simple sentences) but not others (e.g., slow speech). The results should be of interest to both family members and professionals who want to enhance communication and the quality of their interactions with persons with Alzheimer's disease.
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".