‘I’ve heard wonderful things about you’: how patients compliment surgeons
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it