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Record W2190625077 · doi:10.5539/cco.v5n1p11

Skin Cancer and Its Correlates: A Study of Knowledge and Preventive Behavior in Riyadh

2015· article· en· W2190625077 on OpenAlexvenueno aff
Fahad Alamri, Mohammed Y. Saeedi, Arwa Ali, Ahmed K. Ibrahim, Kassim A. Kassim

Bibliographic record

VenueCancer and Clinical Oncology · 2015
Typearticle
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsnot available
Fundersnot available
KeywordsSkin cancerRespondentMedicineCancerDiseaseDemographyIncidence (geometry)Environmental healthGerontologyPathologyInternal medicine

Abstract

fetched live from OpenAlex

Background: Worldwide, the incidence of skin cancer has increased due to increased UV exposure to solar and artificial sources. In Saudi Arabia, skin cancer ranked the 9th most common cancer for both sexes. However, it is considered to be a preventable disease. WHO has proposed several preventive methods to avoid the damaging effects of excessive exposure to UV rays including; social education and adopting positive behavioral changes. The present study aimed to evaluate the level of knowledge, attitudes and behaviors of people in Riyadh city towards skin cancer. Results: The mean respondent's age was 35 years (12-65 years). Females represented about two-thirds of the 341 respondent. A statistically significant associations were detected between awareness about skin cancer with the age (Beta =0.03, p =0.047), educational level (Beta =0.63, p =0.042) and skin color (Beta =-2.14, p<0.001) being significant predictors for disease. Conclusions: To our knowledge, this was the first study to assess the level of knowledge, attitudes, and practices regarding skin cancer in Saudi Arabia. Despite the limitations, the present study’s findings suggested that Saudis lack the sufficient knowledge to understand and assess the importance of skin cancer risk. In addition, the level of knowledge, attitude and behaviors are influenced by several factors as age, education level and skin color.

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 score0.180
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.140
GPT teacher head0.503
Teacher spread0.362 · 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

Citations10
Published2015
Admission routes1
Has abstractyes

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