Experiences of and satisfaction with care provided by male nurses: A convergent mixed‐method study of patients in medical surgical units
Bibliographic record
Abstract
AIM: To understand, comprehensively, patients' experiences of and satisfaction with care provided by male nurses in medical surgical units. BACKGROUND: Patients' positive experiences of and satisfaction with care are plausible indicators of nurses' caring attitude. Female nurses are considered more caring than male nurses and knowledge about patient experiences and satisfaction with care by male nurses is minimal. DESIGN: A convergent mixed methods. METHODS: Patients (N = 262) completed an Urdu language-translated version of the Newcastle Satisfaction with Nursing Scale and 15 participated in semi-structured interviews from August - December 2017. Descriptive statistics were used for quantitative analysis and thematic analysis for qualitative analysis. Both quantitative and qualitative data were merged and integrated for mixed-method analysis. RESULTS: High experience and moderate satisfaction scores were reported. Six themes emerged from care experiences: providing support and comfort, respecting privacy, providing information to patients and families, inability to manage workload and rushing too much, responding late and disturbing sleep. Three themes from satisfaction with nursing care: spending time with patients, capability of providing care and respecting autonomy. It was found that patients' actual experiences and satisfaction with the male nurses' care were considerably better than culturally accepted beliefs and perceptions about the role of men and women in the society. CONCLUSION: Male nurses were caring, but patients' views of nurses' caring attitude were influenced by socio-culture perceptions of the men. Male nurses supported and comforted patients and respected their privacy but did not respond to them on time and were perceived to be authoritarian.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".