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Record W4200540925 · doi:10.1177/10497323211049225

Emotional Health Work of Women With Female Genital Cutting Prior to Reproductive Health Care Encounters

2021· article· en· W4200540925 on OpenAlexafffund
Danielle Jacobson, Daniel Grace, Janice Boddy, Gillian Einstein

Bibliographic record

VenueQualitative Health Research · 2021
Typearticle
Languageen
FieldMedicine
TopicFemale Genital Mutilation/Cutting Issues
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsShameFeelingReproductive healthWorryPsychologyHealth careQualitative researchFemale circumcisionEmotion workNursingMedicineSocial psychologySociologyPsychiatryPopulationGynecologyPolitical science

Abstract

fetched live from OpenAlex

We used institutional ethnography to explore the social relations that shaped the reproductive health care experiences of women with female genital cutting. Interviews with eight women revealed that they engaged in discourse that opposed the practices of cutting female genitals as a human-rights violation. This discourse worked to protect those affected by the practices, but also stigmatized female genital cutting, making participants anticipate experiencing stigmatization during health care. Women's engagement in this discourse shaped their emotional health work to prepare for such encounters. This work included navigating feelings of worry, shame, and courage to understand what to expect during their own appointment; learning from family/friends' experiences; and seeking a clinic with the reputation of best care for women with female genital cutting. It is important to strive for more inclusive health care in which women do not have to engage in emotional health work to prepare for their clinical encounters.

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)0.006
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0100.009
Scholarly communication0.0050.003
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.239
GPT teacher head0.545
Teacher spread0.307 · 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 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

Citations17
Published2021
Admission routes2
Has abstractyes

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