Language-based communication strategies that support person-centered communication with persons with dementia
Bibliographic record
Abstract
BACKGROUND: There are many recommended language-based strategies for effective communication with persons with dementia. What is unknown is whether effective language-based strategies are also person centered. Accordingly, the objective of this study was to examine whether language-based strategies for effective communication with persons with dementia overlapped with the following indicators of person-centered communication: recognition, negotiation, facilitation, and validation. METHODS: Conversations (N = 46) between staff-resident dyads were audio-recorded during routine care tasks over 12 weeks. Staff utterances were coded twice, using language-based and person-centered categories. There were 21 language-based categories and 4 person-centered categories. RESULTS: There were 5,800 utterances transcribed: 2,409 without indicators, 1,699 coded as language or person centered, and 1,692 overlapping utterances. For recognition, 26% of utterances were greetings, 21% were affirmations, 13% were questions (yes/no and open-ended), and 15% involved rephrasing. Questions (yes/no, choice, and open-ended) comprised 74% of utterances that were coded as negotiation. A similar pattern was observed for utterances coded as facilitation where 51% of utterances coded as facilitation were yes/no questions, open-ended questions, and choice questions. However, 21% of facilitative utterances were affirmations and 13% involved rephrasing. Finally, 89% of utterances coded as validation were affirmations. CONCLUSIONS: The findings identify specific language-based strategies that support person-centered communication. However, between 1 and 4, out of a possible 21 language-based strategies, overlapped with at least 10% of utterances coded as each person-centered indicator. This finding suggests that staff need training to use more diverse language strategies that support personhood of residents with dementia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".