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Record W2252904370 · doi:10.1080/03630242.2016.1145169

Contextual factors associated with uptake of breast and cervical cancer screening: A systematic review of the literature

2016· review· en· W2252904370 on OpenAlexaff
Natasha Plourde, Hilary K. Brown, Simone N. Vigod, Virginie Cobigo

Bibliographic record

VenueWomen & Health · 2016
Typereview
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsWomen's College HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineBreast cancerFamily medicinePsycINFOCervical cancerMammographyHealth careReceiptMEDLINECancerGynecologyInternal medicine

Abstract

fetched live from OpenAlex

Existing research on barriers to breast and cervical cancer screening uptake has focused primarily on socio-demographic characteristics of individuals. However, contextual factors, such as service organization, as well as healthcare providers' training and practices, are more feasibly altered to increase health service use. The objective of the authors in this study was to perform a critical systematic review of the literature to identify contextual factors at the provider- and system-level that were associated with breast and cervical cancer screening uptake. Studies published from 2000 to 2013 were identified through PubMed and PsycInfo. Methodologic quality was assessed, and studies were examined for themes related to provider- and system-level factors associated with screening uptake. Thirteen studies met the inclusion criteria. Findings revealed a positive association between patients' receipt of provider recommendation and uptake of breast and cervical cancer screening. Uptake was also higher among patients of female providers. Facilities with flexible appointment times and reminders had higher mammography and Pap test uptake. Similarly, greater organizational commitment to quality and performance had higher breast and cervical cancer screening rates. Knowledge provided in this review could be used in future research to inform the development of public health policy and clinical programs to improve screening uptake.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.035
Threshold uncertainty score0.656

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.000
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)0.0000.000

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 head0.091
GPT teacher head0.381
Teacher spread0.289 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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 routes1
Has abstractyes

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