Patients’ perception of non-standard appearanceof nursing staff
Bibliographic record
Abstract
Introduction: Over the years, nurses have been considered models of care and neatness, dressed in a stiff white uniform.Recently, nursing staff have been choosing uniforms due to the variety of colours and comfort of wearing.Various forms of body decoration are also becoming popular among this professional group.Aim of the study was assessment of patients' perception of non-standard appearance of nursing staff. Material and methods:The study was conducted using the diagnostic survey method using a questionnaire.The study material was collected between March and June 2022.The survey questionnaire consisted of 5 closed and semi-open questions and 5 specific questions.A total of 1267 patients completed the survey questionnaires, and 1109 correctly completed survey questionnaires were analysed.Each respondent gave informed consent to conduct the study.All results are statistically significant when p ≤ 0.05.Results: Most patients were not bothered by nursing staff having tattoos in a visible place (64.8%).Piercing in specific places of the body by nurses was accepted by 55.8% of respondents.One third (33.8%) of respondents were against nursing staff having non-standard hair colours.Most of the respondents believed that nursing staff should be able to wear medical clothing in any colour of their choice (81%).Conclusions: The attitude of patients towards visible body modifications used by nursing staff is individual.Older people, men, and rural residents are less likely to accept the use of forms of body decoration among nursing staff compared to younger people, women, and urban residents.
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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 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".