Accuracy and Congruence of Patient and Physician Weight-Related Discussions: From Project CHAT (Communicating Health: Analyzing Talk)
Bibliographic record
Abstract
OBJECTIVE: Primary care providers should counsel overweight patients to lose weight. Rates of self-reported, weight-related counseling vary, perhaps because of self-report bias. We assessed the accuracy and congruence of weight-related discussions among patients and physicians during audio-recorded encounters. METHODS: We audio-recorded encounters between physicians (n = 40) and their overweight/obese patients (n = 461) at 5 community-based practices. We coded weight-related content and surveyed patients and physicians immediately after the visit. Generalized linear mixed models assessed factors associated with accuracy. RESULTS: Overall, accuracy was moderate: patient (67%), physician (70%), and congruence (62%). When encounters containing weight-related content were analyzed, patients (98%) and physicians (97%) were highly accurate and congruent (95%), but when weight was not discussed, patients and physicians were more inaccurate and incongruent (patients, 36%; physicians, 44%; 28% congruence). Physicians who were less comfortable discussing weight were more likely to misreport that weight was discussed (odds ratio, 4.5; 95% confidence interval, 1.88-10.75). White physicians with African American patients were more likely to report accurately no discussion about weight than white physicians with white patients (odds ratio, 0.30; 95% confidence interval, 0.13-0.69). CONCLUSION: Physician and patient self-report of weight-related discussions were highly accurate and congruent when audio-recordings indicated weight was discussed but not when recordings indicated no weight discussions. Physicians' overestimation of weight discussions when weight is not discussed constitutes missed opportunities for health interventions.
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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 | 0.014 | 0.143 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".