The Use of Tag Questions in Person-centered Communication
Bibliographic record
Abstract
Abstract Tag questions are imperative, declarative, exclamative or interrogative statements that have been modified to include a question (e.g.., It is hot out, isn’t it?). Tag questions have been characterized as elderspeak because it suggests an expected response from the person with dementia, thus limiting his/her ability to make a decision independently. However, tag questions serve multiple functions in conversation. There is limited research on the multidimensional nature of tag questions in conversations between formal caregivers and their clients with dementia. Accordingly, the purpose of this study was to investigate the functions of tag questions used by formal caregivers in utterances coded as person-centered. Conversations (N= 87) between formal caregivers and a simulated person with dementia were video-recorded during a 5-minute care interaction involving morning care. Caregivers’ utterances were coded for the use of the following types of person-centered communication: recognition, negotiation, facilitation, and validation. During secondary data analysis, the person-centered utterances were analyzed for the use and function of tag questions. Conversational analyses revealed two broad functions of tag questions: gather information and facilitate a desired action. Tag questions used to gather information included the following specific functions: acknowledge response, establish common ground, state fact or opinion, initiate topic, conversational joking, state what is being done and questions. Tag questions that facilitated a desired reaction included the following specific functions: offers, advice and suggestions and requests and commands. Findings from the current study reveal that tag questions are not exclusively elderspeak and can be used to illicit conversation.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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 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".