‘I’ve heard wonderful things about you’: how patients compliment surgeons
Bibliographic record
Abstract
This investigation was motivated by physician reports that patient compliments often raise 'red flags' for them, raising questions about whether compliments are being used in the service of achieving some kind of advantage. Our goal was to understand physician discomfort with patient compliments through analyses of audiotaped surgeon-patient encounters. Using conversation analysis, we demonstrate that both the placement and design of compliments are consequential for how surgeons hear and respond to them. The compliments offered after treatment recommendations are neither designed nor positioned to pursue institutional agendas and are responded to in ways that are largely consistent with compliment responses in everyday interaction, but include modifications that preserve surgeons' expertise. In contrast, some compliments offered before treatment recommendations pursue specific treatments and engender surgeons' resistance. Other compliments offered before treatment recommendations do not overtly pursue institutionally-relevant agendas-for example, compliments offered in the opening phase of the visit. We show how these compliments may but need not foreshadow a patient's upcoming agenda. This work extends our understanding of the interactional functions of compliments, and of the resources patients use to pursue desired outcomes in encounters with healthcare professionals.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".