Language-Based Strategies that Support Person-Centered Communication in Formal Home Care Interactions with Persons Living with Dementia
Bibliographic record
Abstract
Language-based strategies are recommended to improve coherence, clarity, reciprocity, and continuity of interactions with persons living with dementia. Person-centered care is the gold standard for caring for persons with dementia. Person-centered communication (PCC) strategies include facilitation, recognition, validation, and negotiation. 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. Analysis of conversation of 30 audio-recorded interactions between personal support workers (PSWs) and persons living with dementia was conducted. The overlap between PCC and language-based strategies was analyzed. Of 11,347 communication units, 2578 overlapped with PCC. For facilitation, 21% were yes/no questions. For recognition, 25% were yes/no questions and 22% were affirmations. For validation, 81% were affirmations and positive feedback. Finally, 60% were yes/no questions for negotiation. The findings highlight the person-centeredness of language-based strategies. PSWs should use diverse language-based strategies that are 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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".