“It's Not What You Say …”
Bibliographic record
Abstract
BACKGROUND: Excellent communication between surgeons and patients is critical to helping patients to make informed decisions and is a key component of both high quality of care and patient satisfaction. Understanding racial disparities in communication is essential to provide quality care to all patients. OBJECTIVE: To examine the content and process of informed decision-making (IDM) between orthopedic surgeons and elderly white versus African American patients. To assess the association of race and patient satisfaction with surgeon communication. RESEARCH DESIGN: Analysis of audiotape recordings of office visits between orthopedic surgeons and patients. PARTICIPANTS: Eighty-nine orthopedic surgeons and 886 patients age 60 years or older in Chicago, Illinois. METHODS: Tapes were analyzed by coders for content using 9 elements of IDM and for process using 4 global ratings of the relationship-building component of communication (responsiveness, respect, listening, and sharing). Ratings by race were compared using chi analysis. Patients completed a questionnaire rating satisfaction with surgeon communication and the visit overall. Logistic analysis was used to assess the effect of race on satisfaction. RESULTS: Overall there were practically no significant differences in the content of the 9 IDM elements based on race. However, coder ratings of relationship were higher on 3 of 4 global ratings (responsiveness, respect, and listening) in visits with white patients compared with African American patients (P < 0.01). Patient ratings of communication and overall satisfaction with the visit were significantly higher for white patients. CONCLUSIONS: The content of IDM conversations does not differ by race. Yet differences in the process of relationship building and in patient satisfaction ratings were clearly present. Efforts to enhance cultural communication competence of surgeons should emphasize the skills of building relationships with patients in addition to the content of IDM.
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.
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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".