An interactional approach to conceptualising small talk in medical interactions
Bibliographic record
Abstract
In medical interactions, it may seem straightforward to identify 'small talk' as casual or social talk superfluous to the institutional work of dealing with patients' medical concerns. Such a broad characterisation is, however, extremely difficult to apply to actual talk, and more specificity is necessary to pursue analyses of how small talk is produced and what it achieves for participants in medical interactions. We offer an approach to delineating a subgenre of small talk called topicalised small talk (TST), derived on the basis of conversation analytically-informed analyses of routine consultations involving orthopaedic surgeons and older patients. TST is a line of talk that is referentially independent from their institutional identities as patients or surgeons, oriented instead to an aspect of the personal biography of one (or both), or to some neutral topic available to interactants in any setting (e.g. weather). Importantly, TST is an achievement of both patient and surgeon in that generation and pursuit of topic is mutually accomplished. In an exploratory but systematic analysis, when this approach was applied to a purposive sample of surgeon-patient interactions, TST was much more prevalent in visits with White than African American patients. Accounts for possible ethnic differences in TST are suggested.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.008 | 0.046 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".