Perception of Health and Its Predictors Among Saudis at Primary Healthcare Settings in Riyadh: Insights from a Cross-Sectional Survey
Bibliographic record
Abstract
Background/Objectives: Despite a link between self-perception of health and morbidity and mortality, data are scarce on factors that can predict one’s health perception, particularly in nations like Saudi Arabia. We conducted a needs assessment to evaluate health perception and identify sociodemographic, behavioural, and comorbidity-related factors influencing health perception among Saudi individuals. Methods: We conducted a cross-sectional survey utilizing an electronic questionnaire that was distributed to 14,239 people who visited primary healthcare centers in Riyadh, Saudi Arabia. We used multiple logistic regression to identify predictors of good health. Data was analyzed using SPSS software. Results: About one-third of the individuals (33.7%) perceived to have excellent health and 35.6% perceived to have very good health. Only 2.1% of the study participants perceived to have poor health. Compared to participants younger than 50 years, those aged 50–75 years were 10% less likely to perceive their health as good (AOR: 0.89, 95% CI: 0.82, 0.97). Males were 1.09 times more likely to perceive their health as good than females (AOR: 1.09, 95% CI: 1.01, 1.18). Smokers were 74% less likely than non-smokers to perceive their health as good (AOR: 0.26; 95% CI: 0.24, 0.29). Obese individuals were 20% less likely to perceive their health in good condition than non-obese individuals (AOR: 0.80; 95% CI: 0.65, 0.98) Individuals with heart disease were about 50% less likely to perceive their health as good condition than those without heart disease (AOR: 0.52; 95% CI: 0.40, 0.76). Conclusions: Despite the high frequency of risk factors, we discovered that Saudis perceive their health to be good on average. However, an independent association between older age, females, smoking, obesity, and heart disease with poor health calls for future epidemiological studies incorporating qualitative dimensions to explore why these individuals with specific risk profiles perceive their health as worse than others.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".