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Record W2897196464 · doi:10.1186/s12905-018-0654-5

Barriers to cervical cancer screening faced by immigrant women in Canada: a systematic scoping review

2018· article· en· W2897196464 on OpenAlexaffabout
Mahzabin Ferdous, Sonya Lee, Suzanne Goopy, Huiming Yang, Nahid Rumana, Tasnima Abedin, Tanvir Chowdhury Turin

Bibliographic record

VenueBMC Women s Health · 2018
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsFoothills Medical CentreAlberta Health ServicesUniversity of Calgary
Fundersnot available
KeywordsImmigrationCervical cancer screeningCervical cancerSystematic reviewMedicineCancerPolitical scienceMEDLINEInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The objective of this scoping study is to review the published literature and summarize findings related to barriers experienced by immigrant women in Canada while accessing cervical cancer screening. METHODS: Electronic databases of peer-reviewed articles and grey literature were searched using comprehensive sets of keywords, without restricting the time period or language. Articles were selected based on the following criteria: (a) the study population consisted of Canadian immigrant women and healthcare providers and other stakeholders serving immigrant women, (b) the research focused on the barriers to accessing cervical cancer screening, and (c) the study was conducted in Canada. RESULTS: Extracted data were grouped and analyzed, resulting in barriers comprised of six themes: economic barriers, cultural barriers, language barriers, healthcare system-related barriers, knowledge-related barriers, and individual-level barriers. Lack of education, low income, preference for a female physician, lack of knowledge, lack of effective communication, and embarrassment were some of the most common barriers mentioned. CONCLUSIONS: Immigrant access to health services, including cervical cancer screening, is a complex issue concerning a wide range of barriers. Our findings offer insights into barriers to cervical cancer screening in immigrant communities in Canada that can be used to assist policymakers, healthcare providers, and researchers enhance the health and well-being of these populations by mitigating barriers and improving 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 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)0.003
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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.033
GPT teacher head0.360
Teacher spread0.326 · 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 designSystematic review
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

Citations111
Published2018
Admission routes2
Has abstractyes

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