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Record W2530805269 · doi:10.1186/s12889-016-3709-2

“I want to save my life”: Conceptions of cervical and breast cancer screening among urban immigrant women of South Asian and Chinese origin

2016· article· en· W2530805269 on OpenAlexafffundabout
Jennifer Hulme, Catherine Moravac, Farah Ahmad, Shelley Cleverly, Aïsha Lofters, Ophira Ginsburg, Sheila Dunn

Bibliographic record

VenueBMC Public Health · 2016
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsWomen's College HospitalSt. Michael's HospitalToronto Public HealthPublic Health OntarioYork UniversityUniversity of TorontoUniversity Health Network
FundersCancer Care Ontario
KeywordsMedicineBiostatisticsImmigrationPublic healthEpidemiologyBreast cancerCervical cancerGerontologyDemographyFamily medicineEnvironmental healthCancerPathologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Breast and cervical cancer screening rates remain low among immigrant women and those of low socioeconomic status. The Cancer Awareness: Ready for Education and Screening (CARES) project ran a peer-led multi-lingual educational program between 2012 and 2014 to reach under and never-screened women in Central Toronto, where breast and cervical cancer screening rates remain low. The objective of this qualitative study was to better understand how Chinese and South Asian immigrants - the largest and most under-screened immigrant groups according to national and provincial statistics - conceive of breast and cervical cancer screening. We explored their experiences with screening to date. We explicitly inquired about their perceptions of the health care system, their screening experiences with family physicians and strategies that would support screening in their communities. METHODS: We conducted 22 individual interviews and two focus groups in Bengali and Mandarin with participants who had attended CARES educational sessions. Transcripts were coded through an iterative constant comparative and interpretative approach. RESULTS: Themes fell into five major, overlapping domains: risk perception and concepts of preventative health and screening; health system engagement and the embedded experience with screening; fear of cancer and procedural pain; self-efficacy, obligation, and willingness to be screened; newcomer barriers and competing priorities. These domains all overlap, and contribute to screening behaviours. Immigrant women experienced a number of barriers to screening related to 'navigating newness', including transportation, language barriers, arrangements for time off work and childcare. Fear of screening and fear of cancer took many forms; painful or traumatic encounters with screening were described. Female gender of the provider was paramount for both groups. Newly screened South Asian women were reassured by their first encounter with screening. Some Chinese women preferred the anonymous screening options available in China. Women generally endorsed a willingness to be screened, and even offered to organize women in their community hubs to access screening. CONCLUSIONS: The experience of South Asian and Chinese immigrant women suggests that under and never-screened newcomers may be effectively integrated into screening programs through existing primary care networks, cultural-group specific outreach, and expanding access to convenient community -based screening.

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)0.005
metaresearch head score (Gemma)0.006
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 score0.055
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0110.017
Scholarly communication0.0050.003
Open science0.0020.006
Research integrity0.0020.004
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 head0.060
GPT teacher head0.346
Teacher spread0.286 · 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

Citations49
Published2016
Admission routes3
Has abstractyes

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