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The “Double Pain” of Childbirth: Immigrant Women's Preference for Female Providers Intrapartum [33Q]

2017· article· en· W2611144943 on OpenAlexaffabout
Christa Aubrey, Zubia Mumtaz, Radha Chari, Peter Mitchell

Bibliographic record

VenueObstetrics and Gynecology · 2017
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineThematic analysisChildbirthImmigrationPreferenceEthnic groupNonprobability samplingFamily medicineQualitative researchNursingPopulationPregnancy

Abstract

fetched live from OpenAlex

INTRODUCTION: Having a male obstetrical provider can be particularly problematic for some immigrant women whose religio-cultural ideals instill a strong preference for female providers. The objective of this study was to gain understanding of the importance and effect of provider gender for immigrant women accessing obstetrical care. METHODS: A focused ethnography was conducted using purposive and convenience sampling of 38 immigrant women from one hospital in Edmonton, Alberta, Canada. Data collection comprised of semi-structured interviews antenatally (38) and postpartum (21), and observation intrapartum (17). Interviews were audio-recorded and transcribed verbatim. Data was managed by a qualitative data analysis software, and analyzed by thematic analysis. RESULTS: Women came from various educational and ethnic backgrounds, but the majority were Muslim (30) and married (36), with a mean age of 27.7 years. All women stated that although they preferred a female, they would accept care from a male provider, as provider competency and desire for a safe birth were most important. A culture of modesty, often interwoven with Islam, informed this preference. Nonetheless, women experienced varying degrees of psychological stress from having received care from a male provider intrapartum, which for a small minority led to considerable, potentially serious consequences. CONCLUSION: As a whole, women are accepting of care from a male provider. However, for a small minority of women, it can be quite detrimental. There is a need to identify those women for whom this is a substantial barrier, so that optimal support can be determined.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.040
GPT teacher head0.304
Teacher spread0.263 · 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 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".

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Citations0
Published2017
Admission routes2
Has abstractyes

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