Bibliographic record
Abstract
BACKGROUND: The nurse-doctor relationship is historically one of female nurse deference to male physician authority. We investigated the effects of physicians' sex on female nurses' behaviour. METHODS: Nurses at an urban, university based hospital completed one of two forms of a vignette-based survey in January, 2000. Each survey included four clinical scenarios. In form 1 of the questionnaire the physicians described were female, male, female, and male. In form 2, vignettes were identical but the physician sex was changed to male, female, male, and female. Differences in responses to questions based on the sex of the physician in each vignette were studied RESULTS: 199 self-selected nurses completed the survey. The responses of 177 female respondents and 11 respondents who did not specifiy their sex, and were assumed to be female based on the overall sex ratio of respondents, were analysed. Persistent sex-role stereotypes influenced the relationship between female nurses and physicians. Nurses were more willing to serve and defer to male physicians. They approached female physicians on a more egalitarian basis, were more comfortable communicating with them, yet more hostile toward them. CONCLUSION: When nurses and doctors are female, traditional power imbalances in their relationship diminish, suggesting that these imbalances are based as much on gender as on professional hierarchy. The effects of this change on the authority of the medical profession, the role of nurses, and on patient care merit further exploration.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".