Power Relations in Older Adults’ Cognitive Interaction in Clinical Setting: A Multimodal Pragmatic Perspective
Bibliographic record
Abstract
Abstract This study explored the construction of power relations in the cognitive assessment of older adults within the Chinese clinical context. Data is derived from audio and video recordings that nine older adults produced in the cognitive assessment of the Chinese version of the Montreal Cognitive Assessment-Basic (MoCA-B), which were then annotated and analyzed from a multimodal pragmatic perspective. The study reveals that examiners and older adults employed various speech acts to achieve distinct communicative goals, with power relations between them being reflected through these speech acts. Examiners tend to claim high power, utilizing discourse strategies such as request, interruption, evaluation, rhetorical questions, and directive speech acts. In contrast, older adults assert high power through directive speech acts, rhetorical questions, and interruptions. Both parties also exhibit low power by using confirming questions and explanations. Additionally, gestures, smiles, prosody features, and other non-verbal communicative resources are synergistically employed to exercise power. The interactive mechanism of constructing power relations reveals that age affects older adults’ power relations construction even in a professional setting of the Chinese context. The negotiation between the advanced age of older adults and the expertise of examiners jointly shapes power relations in their interactions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".