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Record W4409591854 · doi:10.1177/17455057251331263

Breaking the silence: Addressing sexual health challenges among migrant and refugee women

2025· editorial· en· W4409591854 on OpenAlexaff
Zohra S Lassi, Negin Mirzaei, Mumtaz Begum, Jodie Avery, Salima Meherali

Bibliographic record

VenueWomen s Health · 2025
Typeeditorial
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of Alberta
FundersNational Health and Medical Research Council
KeywordsRefugeeReproductive healthSilenceAcculturationMedicineHealth careGender studiesPublic relationsSociologyPolitical sciencePopulationEthnic groupEnvironmental health

Abstract

fetched live from OpenAlex

This editorial addresses the critical yet often overlooked issue of sexual health among migrant and refugee women. With nearly half of the world's 281 million international migrants being women, their unique health challenges demand urgent attention. As a conceptual discussion, this editorial does not present empirical data but rather synthesizes existing literature and expert insights to explore the multifaceted barriers these women face, including financial constraints, language obstacles, cultural taboos, and social exclusion. We examine the complex interplay between acculturation and sexual function, emphasizing how cultural transitions influence sexual well-being. The discussion explores how cultural background shapes sexual attitudes, highlighting the need for culturally sensitive approaches in healthcare delivery. We propose multifaceted solutions, including developing culturally competent healthcare services, implementing targeted education programs, and improving research methodologies. This editorial aims to break the silence surrounding these issues and calls for concerted efforts to address the sexual health needs of migrant and refugee women, ultimately fostering healthier, more equitable societies.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.292
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.036
GPT teacher head0.369
Teacher spread0.333 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations2
Published2025
Admission routes1
Has abstractyes

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