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Record W1607005630 · doi:10.3747/co.v16i5.359

Factors Influencing Mammography Participation in Canada: An Integrative Review of the Literature

2009· article· en· W1607005630 on OpenAlexaffvenueabout
K.A. Hanson, Phyllis Montgomery, D. A. Bakker, Michael Conlon

Bibliographic record

VenueCurrent Oncology · 2009
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsSudbury Regional HospitalLaurentian University
Fundersnot available
KeywordsMedicineFacilitatorEmbarrassmentMammographyInclusion (mineral)Family medicineEthnic groupBreast cancerCancerPsychologySocial psychology

Abstract

fetched live from OpenAlex

This integrative review critically examines quantitative and qualitative evidence concerning factors influencing the participation of Canadian women in mammography. Empirical studies published between 1980 and 2006 were identified and retrieved by searching electronic databases and references listed in published studies. Among the 1461 citations identified and screened, 52 studies met the inclusion criteria and were independently appraised by two researchers. Extracted data were categorized, summarized, compared, and interpreted within and across studies. The presentation of barriers and facilitators to mammography was guided by the Pender Health Promotion Model. Findings from this review showed that no published studies were specific to settings in Saskatchewan, Nova Scotia, Prince Edward Island, Newfoundland and Labrador, and the three Canadian territories. The most common barriers to screening were membership in an ethnic minority and concerns about pain, radiation, and embarrassment. The recommendation of a health care provider for mammography was found to be the most common facilitator for the engagement of women in this health behaviour. The targeting of specific strategies aimed at overcoming identified barriers and the enhancement of facilitators are essential to improving mammography participation rates throughout Canada.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.149
GPT teacher head0.450
Teacher spread0.301 · 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 designObservational
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

Citations58
Published2009
Admission routes3
Has abstractyes

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