ArticleJournal of personalized medicine2022
Predictors of Colorectal Cancer Screening among Average and High-Risk Saudis Population.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 6 citations in OpenAlex.
- Predictors of Colon Cancer Screening Among the Saudi Population at Primary Healthcare Settings in Riyadh.Current oncology (Toronto, Ont.) · 2025Article
- Colonoscopy Uptake in First-Degree Relatives of CRC Patients: Challenges and Intervention Strategies.Patient preference and adherence · 2025Review
- Role of Nanoformulations in the Treatment of Lung Cancer.Recent patents on nanotechnology · 2025Review
- Factors influencing colorectal cancer screening decisions among Saudi women: A qualitative study.PloS one · 2025Article
- From Crypts to Cancer: A Holistic Perspective on Colorectal Carcinogenesis and Therapeutic Strategies.International journal of molecular sciences · 2024Review
- Advances in Personalized Nursing Care.Journal of personalized medicine · 2023Article
- Knowledge and barriers to screening for colorectal cancer among individuals aged 40 years or older visiting primary healthcare clinics in Al-Khobar, Eastern Province.Journal of family & community medicineArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Colorectal cancer (CRC) screening intention is one of the most important elements influencing the longstanding effectiveness of community-based CRC screening programs. The primary purpose of this study is to generate and validate a predictive screening model that investigates the influence of Saudis’ demographics, CRC knowledge, and beliefs on intention to undergo CRC screening via fecal occult blood test (FOBT). Convenience sampling was used to recruit 600 average and high-risk participants from multiple primary health care centers in three major Saudi provinces. A valid and reliable self-administered online survey was used to collect data from March 2021 to October 2021. The final modified screening prediction model explained 57.35% of the variance in screening intention. Intention to screen was significantly influenced by seven factors in which the perceived barriers factor (β = −0.55, p < 0.001) was the strongest predictor. Those who had lower perceived barriers and greater levels of knowledge (β = 0.36, p < 0.001), health motivation (β = 0.35, p < 0.001), perceived benefits of screening (β = 0.35, p < 0.001), severity (β = 0.29, p < 0.001), and susceptibility (β = 0.28, p < 0.001) were more likely to become involved in screening practices. Health care practitioners and various media forms could benefit from the prediction model playing a significant role in raising awareness, reducing perceived barriers, and enhancing Saudi screening rates.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.