NONVERBAL STRATEGIES TO ENHANCE PERSON-CENTERED COMMUNICATION WITH PEOPLE LIVING WITH DEMENTIA
Bibliographic record
Abstract
Abstract Many people living with dementia experience difficulties comprehending language, and benefit from nonverbal communication (NVC). Yet, little published empirical evidence exists for care partners regarding NVC strategies that support person-centered communication with perons living with dementia. This study aimed to determine whether NVC strategies used by personal support workers accompany verbal communication demonstrating person-centered communication indicators (facilitation, negotiation, recognition, validation). Secondary data analysis of video-recorded interactions (n=40) between personal support workers and simulated persons living with dementia was conducted. The recordings were transcribed according to communication-units, which were coded for person-centered communication and NVC, using a novel coding system consisting of ten NVC strategies. The overlap between NVC strategies and verbal person-centered communication was examined. Findings revealed that personal support workers frequently accompany verbal person-centered communication with NVC strategies. Out of 1848 communication-units in which person-centered verbal communication was used, 69% overlapped with NVC strategies. Gaze overlapped with all person-centered communication indicators frequently, both individually (40% – 49% of overlapping communication-units) and when combined with touch (13-24%). Gestures using objects (with and without gaze) frequently accompanied facilitation (17%) and negotiation (21%), while positive facial expressions (with and without gaze) were commonly found in recognition (16%) and validation (16%). The use of NVC strategies which support person-centered communication may lead to communication enhancement, in turn improving interactions and relationships between persons living with dementia and their care partners.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".