PERSON-CENTERED LANGUAGE-BASED STRATEGIES USED BY HOME CARE WORKERS WHO CARE FOR PERSONS LIVING WITH DEMENTIA
Bibliographic record
Abstract
Abstract Several studies recommend language-based strategies for communication with persons living with dementia. Language-based strategies improve coherence, clarity, reciprocity, and continuity of interactions. Person-centered communication (PCC) strategies are the gold standard, including facilitation, recognition, validation, and negotiation. Only one study has examined the overlap between language-based strategies and PCC in long-term care. Little is known about which language-based strategies support PCC in home care. Accordingly, this study investigated the overlap between language-based strategies and PCC in home care interactions. Conversation analysis of 30 audio-recorded routine care interactions between home care workers and persons living with dementia was conducted. The overlap between communication-units coded for PCC and 33 language-based strategies was analyzed. Of 11,347 communication-units, 2,664 overlapped with PCC. For facilitation, 21% were yes/no questions and 15% were announcements of action/intent. For recognition, 25% were yes/no questions and 22% were affirmations. For validation, the majority (81%) of communication-units were affirmations and positive feedback. Finally, for negotiation, 60% of communication-units were yes/no questions. This is the first study examining naturalistic interactions between home care workers and persons living with dementia. The findings highlight the person-centeredness of language-based strategies. Yet only six of 33 language -based strategies occurred in the top 50% of overlapping communication-units. Home care workers in this study use a uniform set of person-centered language-based strategies, illustrated by the frequent use of yes/no questions overlapping with most PCC indicators. Our findings emphasize the need for training among home care workers in the use of diverse language-based strategies that are potentially person-centered.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".