Sexual harassment against female nurses: a systematic review
Bibliographic record
Abstract
BACKGROUND: Sexual harassment is complex and has occupational hazards in nursing. Nurses experienced it than other employees. Female nurses are with the highest rate in the profession. Our aim was to determine the prevalence of sexual harassment against female nurses, the types, perpetrators, and health consequences of the harassment. METHOD: We undertook a systematic review to synthesize quantitative research studies found in Pubmed, Scopus, ProQuest, Web of Science and Google Scholar databases. The studies included were observational, on sexual harassment against female nurse, full text, and published in peer-reviewed English journals up to August 2018. Two independent reviewers searched the articles and extracted data from the articles. The quality of the articles was evaluated using the Modified Newcastle Ottawa Scale for Cross-Sectional Studies Quality Assessment Tool. A descriptive analysis was done to determine the rate of items from the percentages or proportions of the studies. RESULT: The prevalence of sexual harassment against female nurses was 43.15%. It ranged 10 to 87.30%. The 35% of the female nurses were verbally, 32.6% non-verbally, 31% physically and 40.8% were being harassed psychologically. The 46.59% of them were harassed by patients, 41.10% by physicians, 27.74% by patients' family, 20% by nurses and 17.8% were by other coworker perpetrators. The 44.6% of them were developed mental problems, 30.19% physical health problems, 61.26% emotional, 51.79% had psychological disturbance and 16.02% with social health problems. CONCLUSION: The prevalence of sexual harassment against female nurses is high. Female nurses are being sexually harassed by patients, patient families, physicians, nurses, and other coworkers. The harassment is affecting mental, physical, emotional, social and psychological health of female nurses. It is recommended policymakers to develop guidelines on work ethics, legality and counseling programs. Nursing associations to initiate development of workplace safety policy. A safe and secure working environment is needed in the nursing practice and nursing curriculum in prevention strategy. Research is needed on factors associated with sexual harassment. Since only female nurses were the participants, it could not be representative of all nurses. There was no fund of this review.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".