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Record W4213000824 · doi:10.3138/9781487519766-fm

Frontmatter

2019· book-chapter· en· W4213000824 on OpenAlexaffabout
Earle H. Waugh, Shelley Ross, Shirley Schipper

Bibliographic record

VenueUniversity of Toronto Press eBooks · 2019
Typebook-chapter
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsECW Press (Canada)University of Alberta
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Female Doctors in Canada is an accessible collection of articles by experienced physicians and researchers exploring how systems, practices, and individuals might change as the ratio of practising physicians shifts from predominantly male to predominantly female.How will issues such as work hours, caregiving, and doctor-patient relationships be affected?Canadian medical education and health care systems have been based on structures designed by and for men, influencing how women practise, what type of medicine they choose to practise, and how they wish to balance their personal lives with their work.With the goal of opening up a larger conversation, Female Doctors in Canada reconsiders medical education, health systems, and expectations in light of the changing face of medicine.Highlighting the particular experience of women working in the medical profession, the editors trace the history of female practitioners in Canada, while also providing a perspective on the contemporary struggles female doctors face as they navigate a system that was originally tailored to the male experience and that has yet to be modified.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.371
Threshold uncertainty score0.737

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.6370.351

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 head0.027
GPT teacher head0.226
Teacher spread0.198 · 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; the direct Gemma label and the distilled Codex classifier 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
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueUniversity of Toronto Press eBooks→Same topicDiversity and Career in Medicine→French-language works237,207→