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 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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".