Public perception of common cancer misconceptions: A nationwide cross-sectional survey and analysis of over 3500 participants in Saudi Arabia
Bibliographic record
Abstract
Purpose/Background: Patients and healthcare providers use online health information and social media (SM) platforms to seek medical information. As the incidence of cancer rises, the popularity of SM platforms has yielded widespread dissemination of incorrect or misleading information about it. In this study, we aimed to assess public knowledge about incorrect cancer information and how they perceive such information in Saudi Arabia. Methods: A nationwide survey was distributed in Saudi Arabia. The survey included questions on demographics, SM platform usage, and common misleading and incorrect cancer information. Results: The sample (N = 3509, mean age 28.7 years) consisted of 70% females and 92.6% Saudi nationals. Most participants had no chronic illness. One-third were college graduates and less than one-quarter were unemployed. Conclusions: Differences in level of knowledge about cancer emerged in association with different demographic factors. Public trust in health information on SM also led to being misinformed about cancer, independent from educational level and other factors. Efforts should be made to rapidly correct this misinformation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".