Effects of physician gender on patient satisfaction.
Bibliographic record
Abstract
OBJECTIVES: To measure the impact of physician gender on patient satisfaction, controlling for confounding patient variables, and to examine the extent to which differences in satisfaction with male and female physicians can be explained by physician practice styles. METHOD: New adult patients (n=509) were randomized to see male and female primary care physicians at a university medical center outpatient facility. Patient sociodemographics and self-reported health status (using the Medical Outcomes Study Short Form-36) were measured before the initial visit, and satisfaction with the physician was measured immediately following the visit. The entire medical encounter was videotaped and physician practice style was later analyzed using the Davis Observation Code. RESULTS: Female physicians spent a significantly greater proportion of the visit on preventive services and counseling than male physicians did, and male physicians devoted more time to technical practice behaviors and discussions of substance abuse. Visit length was not significantly different for male and female physicians. Patients of female physicians were more satisfied than were those of male physicians, even after adjusting for patient characteristics, visit length, and physician practice style behaviors. CONCLUSION: Patient satisfaction with primary care physicians appears to be influenced not only by patient characteristics and physician behaviors, but also by the gender of the provider. Possible explanations for this may be that psychosocial aspects of the physician-patient interaction are different for male and female physicians. Patients may also bring expectations about female physicians to the encounter, presuming them to be more empathetic, nurturing, and responsive.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".