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Record W2902079336 · doi:10.5539/cco.v7n2p43

Factors That Influence Females’ Intention towards Breast Cancer Early Diagnosis

2018· article· en· W2902079336 on OpenAlexvenueno aff
Jing Huey Chin, Shaheen Mansori

Bibliographic record

VenueCancer and Clinical Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsnot available
Fundersnot available
KeywordsBreast cancerMedicineStructural equation modelingHealth belief modelBreast cancer screeningGynecologyFamily medicineDemographyClinical psychologyPsychologyCancerPublic healthMammographyHealth promotionPathologyInternal medicine

Abstract

fetched live from OpenAlex

Despite the advanced medical technology nowadays, breast cancer incidence rate is still increasing worldwide. This is due to females lack of knowledge and awareness about the breast screening. Therefore, the objective of this study is to explore the factors that influence females’ intention towards breast cancer early diagnosis with health belief model and provide practical recommendations. To evaluate the proposed hypotheses in this study, 600 self-administrated questionnaires were distributed to Malaysian female who is 18 years old and above with the usage of non-probable sampling approach (convenience sampling approach). Given that, the findings demonstrate perceived barriers has the greatest impact towards females’ breast cancer early diagnosis intention, followed by perceived severity and perceived susceptibility. The contribution of this study is to evaluate the relationships between perceived severity, perceived susceptibility and perceived barriers towards females’ breast screening intention with the mediating variable of attitude. To support this, health believe model and theory of planned behaviour are employed in this research.

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 score0.223
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

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

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 head0.293
GPT teacher head0.505
Teacher spread0.212 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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