Male patients’ gender preferences for hospital nurses
Bibliographic record
Abstract
There has been limited research exploring the beliefs and attitudes of male patients regarding the gender of their nurses. These attitudes, as well as the factors affecting the gender-preference of male patients, must be explored in a flexible, holistic manner. The objective of our study was to explore key aspects of male patients’ beliefs and attitudes about the gender of their nurses in the hospital setting, as well as the factors that influenced those perceptions. We employed a descriptive, qualitative, cross-sectional design. Data were collected through one-on-one interviews, which were transcribed verbatim. A deductive and inductive approach using content analysis of each question was used to analyse the data. Ten male patients were interviewed. Initially, participants reported no gender preference for their nurses. The majority agreed that the nature of the task did not matter in their preference for a male or female nurse. Most suggested that females were inherently better suited to nursing than males due to their ability to be caring, nurturing, and detail-oriented. Bussey and Bandura’s Social Cognitive Theory of Gender Development and Differentiation was supported and provided a suitable framework for the study. There is a need for educational institutions to determine new ways to teach male nursing students to be caring, nurturing, and detail-oriented. Whether nurses are male or female, having a caring approach is important to patients, as well as possessing other ‘ideal’ characteristics.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".