Promoting the maintenance of satisfactory communication: strategies used by caregivers and medical staff with people suffering from Alzheimer's disease
Bibliographic record
Abstract
The goal of this literature review was to determine communication strategies that foster interactions between individuals with Alzheimer's disease (AD) and caregivers or medical staff. A search of PubMed and PsycINFO databases was conducted using combinations of the following terms: Alzheimer, family, caregiver, communication, language, treatment, intervention, therapy, communication strategies, functional communication and daily living activities. Studies were chosen based on the original language of the text, year of publication, peer-review status and specificity of the results regarding communication strategies in AD. This review covers 14 articles published between 1994 and 2014. The in-depth analysis of these studies shows that various communication strategies used by caregivers and medical staff are effective to promote interactions with people with AD at different stages of the disease. For caregivers, these strategies include producing short and syntactically simple sentences, relying on semantic memory rather than on episodic memory, providing more information and reformulate, removing potential distractions from the environment, specifying the topic of the conversation, using precise terms to identify communication breakdowns. Other strategies also appeared to be effective for medical staff, such as asking close-ended questions, providing response choices, verbalizing the actions performed during medical care and activities, paying attention to the needs expressed through verbal and non-verbal messages, adopting a supportive way of speaking. Furthermore, other communication strategies rather appear to be harmful to functional communication. These harmful strategies involve insisting that the person produce a specific word, asking him/her to repeat or tune out in case of communication breakdown. Finally, contradictory outcomes are reported for some strategies such as speaking slowly, repeating or rephrase questions and information. This article provides some useful avenues to better interact with people with AD. Further studies are needed to confirm and refine some results, in particular as regards the effectiveness of communication strategies at the different stages of the disease through longitudinal research design.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".