The Prevalence of Headache and Associated Factors in Al-Kharj, Saudi Arabia: A Cross-Sectional Study
Bibliographic record
Abstract
Background. Only few studies have investigated the prevalence and risk factors of headaches among the Saudi population. The study aimed to estimate the prevalence of headache and to explore its associated risk factors Al-Kharj, Saudi Arabia. Methods. The multistage sampling technique was used to enroll 1200 population-based participants who were asked to complete a self-administered questionnaire about headaches, demographics, and several other parameters such as smoking status and different chronic and psychological illnesses. The chi-square test and multivariate logistic regression analysis were used to test the association. Results. The overall prevalence of headaches in this study was 3%. The multiple logistic regression analysis showed that females were more likely to have headaches than males (odds ratio (OR) 0.735, 95% confidence interval (CI) = 0.612–1.341; P = 0.024 ). Being a current smoker was also significantly associated with higher “odds” of having headache (OR = 1.319, 95% CI = 0.932–2.462; P = 0.037 ). Participants who were overweight had a significantly higher risk of headache (OR = 1.631, 95% CI = 1.48–1.854; P = 0.037 ). Nonmarried people were significantly more likely to have headache pain, compared to married individuals (OR = 0.875, 95% CI = 0.646–2.317; P = 0.047 ). Conclusion. The prevalence of headaches was 3%, and four significant associated factors were identified: females, nonmarried, smoking, and overweight. The temporality of the relationship between these factors and headache cannot be confirmed in this cross-sectional study; so future longitudinal studies are needed to confirm these potential causal relationships.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".