Discourse structure differences in lay and professional health communication
Bibliographic record
Abstract
Purpose In this paper the authors seek to compare lay (consumer) and professional (physician) discourse structures in answers to diabetes‐related questions in a public consumer health information website. Design/methodology/approach Ten consumer and ten physician question threads were aligned. They generated 26 consumer and ten physician answers, constituting a total dataset of 717 discourse units (in sentences or sentence fragments). The authors depart from previous LIS health information behaviour research by utilizing a computational linguistics‐based theoretical framework of rhetorical structure theory, which enables research at the pragmatics level of linguistics in terms of the goals and effects of human communication. Findings The authors reveal differences in discourse organization by identifying prevalent rhetorical relations in each type of discourse. Consumer answers included predominately (66 per cent) presentational rhetorical structure relations, those intended to motivate or otherwise help a user do something (e.g. motivation, concession, and enablement). Physician answers included mainly subject matter relations (64 per cent), intended to inform, or simply transfer information to a user (e.g. elaboration, condition, and interpretation). Research limitations/implications The findings suggest different communicative goals expressed in lay and professional health information sharing. Consumers appear to be more motivating, or activating, and more polite (linguistically) than physicians in how they share information with consumers online in similar topics in diabetes management. The authors consider whether one source of information encourages adherence to healthy behaviour more effectively than another. Originality/value Analysing discourse structure – using rhetorical structure theory – is a novel and promising approach in information behaviour research, and one that traverses the lexico‐semantic level of linguistic analysis towards pragmatics of language use.
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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 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".