MétaCan
Menu
Retour à la cohorte
Enregistrement W2940987862 · doi:10.1111/jpc.14460

Feminism, equity and the family‐centred workplace

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

Notice bibliographique

RevueJournal of Paediatrics and Child Health · 2019
Typeeditorial
Langueen
DomaineSocial Sciences
ThématiqueDiversity and Career in Medicine
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésFeminismMedicineOppressionGender studiesDisadvantagedTheme (computing)Promotion (chess)Equity (law)Health carePoliticsSociologyPolitical scienceLaw

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,006
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,013
Score d'incertitude au seuil0,042

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,006
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0060,024
Communication savante0,0060,005
Science ouverte0,0010,007
Intégrité de la recherche0,0030,004
Charge utile insuffisante (le modèle a refusé de juger)0,0130,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,021
Tête enseignante GPT0,318
Écart entre enseignants0,297 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2019
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueJournal of Paediatrics and Child HealthMême sujetDiversity and Career in MedicineTravaux en français237 207