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Record W2007376577 · doi:10.1258/j.jmb.2007.s-1-06-05

Was Osler opposed to women becoming doctors?

2007· article· en· W2007376577 on OpenAlexaboutno aff
Neil McIntyre

Bibliographic record

VenueJournal of Medical Biography · 2007
Typearticle
Languageen
FieldMedicine
TopicHistory of Medical Practice
Canadian institutionsnot available
Fundersnot available
KeywordsOpposition (politics)Medical schoolWomen PhysiciansMedicineFamily medicineFirst world warWorld War IIGender studiesHistoryMedical educationSociologyLawPolitical sciencePoliticsAncient history

Abstract

fetched live from OpenAlex

In 1885 William Osler (1849-1919) expressed his opposition to women-only medical schools in Canada arguing there was no market for female practitioners. He believed women were not strong enough for clinical practice. He was unhappy that the Johns Hopkins University was forced to accept women when its Medical School opened in 1893. At Oxford he continued to express concern about the fitness of women for practice and suggested their best opportunities lay in a limited number of areas--pathology, institutions for women, the care of women and children in general practice, and in India and the missionary field. His views seemed to change during the First World War, when he helped to raise funds for the London School of Medicine for Women and supported Charles Sherrington's attempt to get women admitted to the Oxford Medical School. However, in his last year he was again concerned about the relatively poor quality of women physicians--but at least thought it unsurprising considering how badly they had been treated!

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)0.005
metaresearch head score (Gemma)0.011
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: none
Teacher disagreement score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.028
Scholarly communication0.0090.007
Open science0.0010.004
Research integrity0.0080.012
Insufficient payload (model declined to judge)0.0070.002

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 head0.028
GPT teacher head0.351
Teacher spread0.323 · 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

Citations1
Published2007
Admission routes1
Has abstractyes

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