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Record W7137640251

Gender Bias in Nursing: A Scoping Review

2023· other· en· W7137640251 on OpenAlexaboutno aff
Emma Gils

Bibliographic record

VenueTuwhera (Auckland University of Technology) · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsJudgementTransgenderGender biasGender identityInclusion (mineral)MEDLINEPower (physics)Doing genderIndigenousGender history
DOInot available

Abstract

fetched live from OpenAlex

Background: Gender bias is preference and preferential treatment for one gender over another. This has problematic implications for women and is well documented through the gender pay gap, the lack of women in leadership positions, and the amount of unpaid care work women undertake compared with men. This review examined how gender operates in nursing prompted by an awareness that much discussion about gender is about men, and this led to curiosity about how women and transgender people are represented and what their experiences are in relation to gender. This scoping review critically evaluated the research and grey literature to present an in-depth discussion of gender power in the nursing profession, with a particular view on Aotearoa New Zealand. Methods: A scoping review was conducted. CINAHL, Scopus, Medline via EBSCO, Business Source Complete and Joanna Briggs Institute databases were searched with inclusion criteria of research from New Zealand, Australia, Canada, and the United States of America with an aim to establish a possible relationship between gender, nursing and Indigenous or First Nation people. Grey literature was included from the websites of New Zealand, Canada, Australia, and the United States nursing unions and registering boards. Results: The findings highlighted four main themes of 1) touch, 2) money, 3) glass elevator and glass ceiling, and 4) gender identity and assumptions of nurses. 1) Men in nursing experience judgement from patients when required to perform touch as part of their clinical duties and this judgement may influence the area where a male nurse will work. 2) In careers where women are the majority and significantly outnumber men, gender bias exists for men to occupy more senior positions and better paid jobs. 3) Women in nursing experience a glass ceiling effect that prevents upward promotion yet men in nursing experience a glass escalator effect whereby they are more largely represented at leadership and management levels. 4) In nursing, gender bias is also present due to gender stereotypes of nursing being seen as women’s work, and men in nursing are subject to negative stereotypes. Sub-themes within ‘gender identity and assumptions of nurses’ are gender role strain, gender as active challenge to social norms, sexuality, and the need for muscle. Men in nursing are often stereotyped as homosexual or less compassionate than their female peers and report frustration at the gender-based assumptions they are faced with. Discussion: Men and women in nursing experience gender bias which operates in different ways. The research focuses on the experiences of men, leaving gaps in the literature for experiences of women, and people who are not cisgendered. Very minimal intersectionality occurred, leaving experiences of gender bias within nursing and people of colour, minority groups and religious perspectives mostly absent. The research focus of men in nursing reinforces the viewpoint that men’s experiences are more valuable than women’s, which strengthens the gender bias found in most healthcare systems. Additional research is required to include women, transgender, gender non-binary, and gender-neutral people’s struggles of gender bias within nursing and a strong focus is recommended to recognise the weight of intersectionality within such research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.526
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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.

Opus teacher head0.050
GPT teacher head0.299
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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