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Record W4401051619 · doi:10.1177/23333936241266997

Cervical Cancer Screening Uptake and Experiences of Black African Immigrant Women in Canada

2024· article· en· W4401051619 on OpenAlexaffabout
Abosede Catherine Ojerinde, Sally Thorne, A. Fuchsia Howard, Arminée Kazanjian

Bibliographic record

VenueGlobal Qualitative Nursing Research · 2024
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCervical cancerImmigrationQualitative researchPerceptionMedicineEthnic groupBlack womenPopulationFamily medicineCancer screeningCancerCervical cancer screeningGender studiesPsychologySociologyEnvironmental healthPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Cervical cancer is one of the leading causes of cancer-related death among African women. Unfortunately, in most sub-Saharan African nations, women are vulnerable if they are unaware that cervical cancer is preventable with frequent screening and early treatment. The aim of this study was to examine Black African immigrant women's perceptions and experiences of cervical screening in British Columbia, Canada. Twenty Black African immigrant women were interviewed using the qualitative research method Interpretive Description. Data collection approaches included indepth interviews and analytic memos. Data were analyzed using a constant comparative technique guided by a socioecologic framework to capture subjective experiences and perceptions. Four key themes were identified, including confusing conceptualizations about cancer and cancer screening, competing priorities, concerns for modesty, and commitment to culture. The study findings point to the need for more active approaches to promoting cervical screening for this population.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.118
GPT teacher head0.499
Teacher spread0.381 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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