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Record W1510201876 · doi:10.1111/gwao.12007

Gender and Supportive Co‐Worker Relations in the Medical Profession

2013· article· en· W1510201876 on OpenAlexfundno aff
Jean E. Wallace

Bibliographic record

VenueGender Work and Organization · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsnot available
FundersVedecká Grantová Agentúra MŠVVaŠ SR a SAVAlberta Heritage Foundation for Medical ResearchAlberta Health Services
KeywordsTokenismHomophilyEmpathyEmotional supportRepresentation (politics)PsychologySocial supportMedical professionSocial psychologyMedical educationMedicineSociologyPolitical science

Abstract

fetched live from OpenAlex

Women's growing numerical representation in the professions has not necessarily translated into women being truly integrated in these occupations. Questionnaire data are used to examine whether female physicians are socially integrated in the male‐dominated profession of medicine in terms of the support they receive from their medical colleagues compared to male physicians. The literature on tokenism and homophily suggests that women in male‐dominated professions receive less support than their male colleagues, whereas the social support literature predicts that women typically receive more emotional support than men but less informational and instrumental support. The results of this study shed light on the complex and multi‐layered ways in which gender is relevant to our understanding of the extent to which co‐workers provide empathy, information and assistance to one another.

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)0.002
metaresearch head score (Gemma)0.008
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.056
GPT teacher head0.294
Teacher spread0.238 · 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
GenreEmpirical

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

Citations40
Published2013
Admission routes1
Has abstractyes

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