The role of gender in patient preference for breast surgical care – a comment on equality
Bibliographic record
Abstract
Gender preference among patients seeking medical care is an issue that is not well understood. It warrants exploration, particularly for patients undergoing sensitive physical exams. In a recent IJHPR article, Groutz et al. reported a survey study that explored patient preferences in selecting a breast surgeon. They found that a third of patients preferred a female surgeon for their breast examination. However, surgical ability was the primary factor in selecting a surgeon for their breast surgery. This commentary discusses these findings in the context of patient-centered care and issues of gender equality in medical education.Gender equality is considered an important societal movement in achieving human rights for everyone based on their ability, rather than their gender and opportunity. This commentary argues that the goal of gender equality is why women should be encouraged to enter surgical professions, recognizing that patient preferences will be shaped by societal norms. Gender preferences for the performance of sensitive physical examinations by some patients are likely multifactorial and they warrant more exploration to deliver ideal patient centered care.
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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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.062 | 0.046 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".