Approaches of male nurses for degendering nursing and becoming visible: A metasynthesis
Bibliographic record
Abstract
INTRODUCTION: Patient, nurse and public discrimination against male nurses is well-documented. Male nurses encounter challenges in degendering nursing and becoming more visible nursing professionals. However, little is known about male nurses' decisions and strategies to address issues concerning gender-based discrimination and professional visibility. AIMS: To develop a comprehensive understanding of the approaches of male nurses to degender nursing and become more visible in the profession. METHODS: A metasynthesis using the PRISMA guidelines. A comprehensive literature search was performed PubMed, CINAHL, Web of Science and Scopus databases. The search terms included 'lived experiences', 'male nurses', 'clinical practice', 'male educators' and 'male nurse managers'. In total, 16 qualitative studies published from January 2005 to November 2020 were critically appraised and synthesised. Two authors independently extracted data using summary tables. Data were synthesised using thematic synthesis which entails generation of codes and development of descriptive and analytical themes. RESULTS: Male nurses used intrapersonal, interpersonal and strategic adaptation to degender nursing and becoming visible as a nursing professional. These approaches entailed goal-directed behaviours, relationship-building strategies and positive and negative manipulation of resources. DISCUSSION: The approaches to degender nursing enabled men to prove their worth as competent nursing professionals, lessen the gendered stigmatisation and receive autonomy and respect. CONCLUSIONS: Degendering the gendered stigmatisation of nursing and becoming a visible nursing professional is essential for male nurses to promote diversity in the profession. Male nurses used both negative and positive approaches to gain recognition, respect and autonomy. They continued their struggles to promote the role of men as nurses. RELEVANCE TO CLINICAL PRACTICE: Nursing institutions and regulatory bodies could use the findings to develop conducive environments to degender nursing and increase male nurses' visibility. Future male nurses could use the identified positive approaches and tailor them to meet their needs to become more visible and competent nursing professionals.
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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 | 0.045 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.033 | 0.021 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".