MétaCan
Menu
Back to cohort
Record W2188311447 · doi:10.1177/1049732315619373

Sociocultural Influences on Arab Women’s Participation in Breast Cancer Screening in Qatar

2015· article· en· W2188311447 on OpenAlexaff
Jasmine Hwang, Tam Truong Donnelly, Carol Ewashen, Elaine McKiel, Shelley Raffin, Janice Kinch

Bibliographic record

VenueQualitative Health Research · 2015
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsUniversity of Calgary
FundersQatar National Research Fund
KeywordsBreast cancerSociocultural evolutionThematic analysisContext (archaeology)MedicineBreast cancer screeningHealth carePsychological interventionQualitative researchGerontologyFamily medicinePsychologyCancerNursingMammographySociologyPolitical scienceGeographyInternal medicine

Abstract

fetched live from OpenAlex

Breast cancer, the most common cancer among Arab women in Qatar, significantly affects the morbidity and mortality of Arab women largely because of low participation rates in breast cancer screening. We used a critical ethnographic approach to uncover and describe factors that influence Arab women's breast cancer screening practices. We conducted semistructured interviews with 15 health care practitioners in Qatar. Through thematic analysis of the data, we found three major factors influencing breast cancer screening practices: (a) beliefs, attitudes, and practices regarding women's bodies, health, and illness; (b) religious beliefs and a culturally sensitive health care structure; and (c) culturally specific gender relations and roles. Arab women's health practices cannot be understood in isolation from the sociocultural environment. The problem of low rates of breast cancer screening practices and supportive interventions must be addressed within the context and not be limited to the individual.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.696
GPT teacher head0.660
Teacher spread0.035 · 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

Citations39
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueQualitative Health ResearchSame topicGlobal Cancer Incidence and ScreeningFrench-language works237,207