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Record W4383066622 · doi:10.1503/cjs.022121

Surgical sexism in Canada: structural bias in reimbursement of surgical care for women

2023· article· en· W4383066622 on OpenAlexaffvenueabout
Michael Chaikof, Geoffrey W. Cundiff, Fariba Mohtashami, Alexi Millman, Maryse Larouche, Marianne Pierce, Erin A. Brennand, Colleen D. McDermott

Bibliographic record

VenueCanadian Journal of Surgery · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsUniversity of TorontoUniversity of CalgaryMcGill UniversityUniversity of British ColumbiaDalhousie University
Fundersnot available
KeywordsMedicineReimbursementFamily medicineHealth carePatient careMEDLINENursing

Abstract

fetched live from OpenAlex

BACKGROUND: It is well established that female physicians in Canada are reimbursed at lower rates than their male counterparts. To explore if a similar discrepancy exists in reimbursement for care provided to female and male patients, we addressed this question: Do Canadian provincial health insurers reimburse physicians at lower rates for surgical care provided to female patients than for similar care provided to male patients? METHODS: Using a modified Delphi process, we generated a list of procedures performed on female patients, which we paired with equivalent procedures performed on male patients. We then collected data from provincial fee schedules for comparison. RESULTS: In 8 out of 11 Canadian provinces and territories studied, we found that surgeons were reimbursed at significantly lower rates (28.1% [standard deviation 11.1%]) for procedures performed on female patients than for similar procedures performed on male patients. CONCLUSION: The lower reimbursement of the surgical care of female patients than for similar care provided to male patients represents double discrimination against both female physicians and their female patients, as female providers predominate in obstetrics and gynecology. We hope our analysis will catalyze recognition and meaningful change to address this systematic inequity, which both disadvantages female physicians and threatens the quality of care for Canadian women.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.082
GPT teacher head0.289
Teacher spread0.207 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations21
Published2023
Admission routes3
Has abstractyes

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