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Record W2030478551 · doi:10.5339/avi.2015.3

Promoting screening to reduce breast cancer mortality among Arab women: What do healthcare professionals need to do?

2015· article· en· W2030478551 on OpenAlexaff
Tam Truong Donnelly, Al-Hareth Al Khater, Salha Bujassoum Al‐Bader, Mohamed Ghaith Al‐Kuwari, Mariam Abdulmalik, Nabila Al-Meer, Rajvir Singh

Bibliographic record

VenueAvicenna · 2015
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBreast cancerMedicinePsychological interventionIntervention (counseling)Breast cancer screeningFamily medicineHealth careCancerPublic healthCancer screeningMammographyGerontologyNursingEnvironmental healthInternal medicinePolitical science

Abstract

fetched live from OpenAlex

Breast cancer (BC) is the most common cancer among Arab women. Early detection of breast cancer through regular screening activities, improvement of the quality of screening activities, and enhanced treatment have been found to decrease mortality rates. However, alarmingly low participation rates in breast cancer screening activities have been reported among Arab women. Drawing on the findings of our recent study in Qatar and a comprehensive literature review of studies, in this paper, we recommend several categories of intervention strategies to promote early detection of breast cancer among Arab populations. These include: (1) Providing public education about breast cancer and cancer screening methods; (2) Encouraging primary care physicians to incorporated BC screening recommendations into their daily practice and routine with their female patients; (3) Deliver interventions that minimize cognitive barriers at the individual level; (4) Incorporate access-enhancing strategies; and (5) More intervention and evaluation studies are needed to develop culturally sensitive interventions and assess the cost-effectiveness and long-term sustainability of the intervention programs.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.093
GPT teacher head0.412
Teacher spread0.319 · 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 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

Citations7
Published2015
Admission routes1
Has abstractyes

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