Examining Success of Communication Strategies Used by Formal Caregivers Assisting Individuals With Alzheimer’s Disease During an Activity of Daily Living
Bibliographic record
Abstract
PURPOSE: To examine how formal (i.e., employed) caregivers' use verbal and nonverbal communication strategies while assisting individuals with moderate to severe Alzheimer's disease (AD) during the successful completion of an activity of daily living (ADL). Based on the literature, the authors hypothesized that caregivers' use of 1 proposition, closed-ended questions, and repetition would be of most benefit. METHOD: Twelve caregiver-AD dyads participated in this observational study. Each dyad was videorecorded on 6 separate occasions while completing handwashing. Handwashing sessions were transcribed and systematically coded for the use of communication strategies during completion of the ADL. RESULTS: Caregiver-AD dyads successfully completed 90% of all handwashing sessions, and caregivers employed a variety of communication strategies. Consistent with our hypotheses, during successful task completion, caregivers most frequently provided individuals with AD with 1 direction or idea (i.e., proposition) at a time, closed-ended questions, and paraphrased repetition. Caregivers also frequently used encouraging comments and the resident's name during the task; however, use of these strategies was not correlated to task success rate. CONCLUSION: This study adds to the limited body of evidence supporting the use of specific communication strategies by caregivers assisting individuals with moderate to severe AD during successful completion of ADLs.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".