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Record W4285018905 · doi:10.22215/etd/2022-14966

Do No Harm? Discrepancies across Canadian Healthcare Policy and Obstetrics and Gynaecology Training for Treating Women affected by Female Genital Cutting

2022· dissertation· en· W4285018905 on OpenAlexafffundabout
Hailey Johnston

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicFemale Genital Mutilation/Cutting Issues
Canadian institutionsCarleton University
FundersSocial Sciences and Humanities Research Council of CanadaPublic Health Agency of Canada
KeywordsHarmFemale circumcisionHealth careCovertFraming (construction)Obstetrics and gynaecologyMedicineMedical educationNursingPsychologyPolitical scienceFamily medicineGynecologySocial psychologyLawEngineering

Abstract

fetched live from OpenAlex

Female genital cutting (FGC) is a flashpoint feminist issue. It is a deeply stigmatized and controversial cultural practice that affects millions of women and girls internationally and is criminalized in Canada. My research set out to answer the questions: To what extent is obstetrics and gynaecology training in Canada structured to provide adequate, effective, and culturally sensitive care for women who have undergone FGC? Does this training reflect the broader framing of FGC as a "barbaric" practice? Through my research into medical education, I find that Canadian healthcare reflects norms and values of nationalism when considering whose bodies represent these standards. With the methodology of transnational feminism, these covert issues of structural violence that mark certain bodies as Other become more clearly recognizable. I challenge predominant knowledge, attitudes, and skills of healthcare practitioners to ultimately recommend four steps toward creating a new benchmark for culturally sensitive training and care.

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)0.000
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.350
Teacher spread0.322 · 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.

Study designQualitative
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

Citations0
Published2022
Admission routes3
Has abstractyes

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