Professional Comportment: Nurses, Patients and Family Survey
Bibliographic record
Abstract
AIM: The aim of the study was to survey nurses, patients and families regarding their perceptions of nursing attire, identification and professional image. BACKGROUND: Recent changes in uniform policies have made it difficult for patients to identify the nurse. METHOD: A convenience sample of nurses (RNs, RPNs) and patients and families from an acute care facility in Canada were surveyed. Surveys included a combination of forced-choice questions and open text boxes. Quantitative data were analyzed, and a thematic content analysis was conducted. RESULTS: The nurse survey resulted in a 64% (n=642) response rate; the patient/family survey response rate was 70% (n=30). Fifty-three per cent of the patient/family advisory team members reported that the nurses did not look professional in comparison to 95% of the nurses who indicated they did. Three key themes emerged: professional image, nurse identification and adoption of a standardized uniform. CONCLUSION: Professional comportment of nurses includes attire suitable for the clinical area that reflects a professional image and allows patients and families to identify the nurses. IMPLICATIONS FOR NURSING LEADERSHIP: This study identified the need to engage nurses, patients and families to ensure professional comportment when uniform polices are developed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".