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Record W2940987862 · doi:10.1111/jpc.14460

Feminism, equity and the family‐centred workplace

2019· editorial· en· W2940987862 on OpenAlexaboutno aff
David Isaacs

Bibliographic record

VenueJournal of Paediatrics and Child Health · 2019
Typeeditorial
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsFeminismMedicineOppressionGender studiesDisadvantagedTheme (computing)Promotion (chess)Equity (law)Health carePoliticsSociologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Women are significantly less likely than men to be appointed to senior management roles; those women who do succeed are paid substantially less than their male counterparts.1 Recognising both that women in health care are under-represented in positions of power and leadership and that this gender inequality is harmful to science, medicine and global health, The Lancet decided to publish a theme issue on feminism. Their call for papers yielded over 300 submissions from over 40 countries; the theme issue was published in February 2019.2 A leading article entitled ‘Feminism is for everybody’ quoted African American feminist bell hooks (no capitals) who wrote a book of the same name in 1952.2 Although there is no single agreed definition of feminism, hooks said that to be feminist meant ‘to want for all people, male and female, liberation from sexist role patterns, domination, and oppression’. Men can be feminists. An important theme that emerged in the Lancet issue was that systemic, often implicit, bias against women in health has persisted and results in women being disadvantaged in being promoted and in being rewarded financially compared with men.2 In academia, women are less likely to obtain grant funding, be published and obtain promotion.2 Women face unique, often unmet challenges in the workplace. Why does gender bias matter? It matters to science. More gender-diverse and inclusive teams improve outcomes in science and medicine.2-4 It matters to patients. A Florida study found female patients with acute myocardial infarction were more likely to survive if they were treated by female than by male doctors.5 Male and female patients treated by female doctors had similar outcomes, suggesting a unique problem for male doctors treating female patients. The outcome difference was attenuated for male doctors who had more exposure to female patients and female physicians.5 Studies showed patients had better outcomes when treated by female surgeons in Canada and women internists in Japan.4 The postulated explanation is that gender is a marker of behaviours that lead to better outcomes: female doctors spend more time with patients, follow guidelines more closely and have better communication skills than their male counterparts.4 Gaps in gender equality are narrowing globally, but significant challenges persist in all countries. Approaches to improve gender equality need to be made by individuals and at the organisational level. Egalitarian men can do their utmost to promote opportunities for women in medicine and science. But to quote feminist Mary Beard, ‘you cannot easily fit women into a structure that is already coded male; you have to change the structure’. Implicit gender bias in academia results in men being consistently judged to be superior to women in terms of skills, productivity, work and leadership on the basis of gender alone. We need to dispel the ‘myth of meritocracy’ perpetuated by those within the hierarchy who have a vested interest in excluding people on the basis of gender or race. The change must be real: many institutions put forth blithe statements about equity, belied by persisting inequity. Often, the best way to improve the lot of women trainees affected unfairly by excessive work hours or work demands is to improve the lot of all trainees. This way ensures that excess work burden does not fall on less represented groups, such as women or people of colour, the so-called ‘minority tax’. We need to make systematic changes in medical education, in the way we treat men and women in the workplace, in the way we promote men and women in academia and in equality of remuneration. Paediatrics focuses on children and their families, yet too often we neglect the needs of our young paediatricians with young families.6 Women's usually temporary exit from the workplace to bear and care for children is a major factor in their career trajectory: they fall behind and never catch up. Women who leave surgical training report not only long working hours but also sleep deprivation, bullying, discrimination, sexism and sexual harassment.7 They lack sufficient supports and suitable role models. They are also disproportionately affected by the impact of pregnancy and childbirth and child rearing.7 Society has competing demands: productivity and family. We need to challenge hierarchical medical specialist models that fail to promote specialist training for women of reproductive age due to patriarchal beliefs that to do so would take a training opportunity away from a ‘more diligent and career-minded’ male who will not suspend their training or ask for time off to bear and rear children. Change will require increased flexibility for reproductive choice, family/life balance and child care opportunities (funding support and places) in medical institutions. A colleague who worked in the Netherlands was impressed by the recognition of the importance of having at least one parent at home caring for the baby reflected in generous provision of job-sharing opportunities to accommodate two working parents. Paediatricians should struggle for a truly family-centred workplace, one in which all staff with families feel supported to rear their own children. Such a workplace would be equitable and would respect feminist principles. My thanks to Tony Delamothe, Friedericke Eban, Melanie Jansen, Ben Marais, Ken Nunn and Anne Preisz for their insights on earlier versions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.024
Scholarly communication0.0060.005
Open science0.0010.007
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0130.001

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.

Opus teacher head0.021
GPT teacher head0.318
Teacher spread0.297 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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