Households Health Status in Saudi Arabia: Spatial Distribution and Association With Socioeconomic Factors
Bibliographic record
Abstract
This paper examines the spatial relationship between Saudi and non-Saudi people's health status and the socioeconomic composition of the neighbourhoods in which they live. Data were recorded from the National Population Health Survey (NPHS) performed by the Saudi General Authority for Statistics (GAS) in 2018. The survey counts 23,980,846 inhabitants grouped into 24,012 households who assessed their health status by gender and administrative region. Only people who are fifteen years of age and over and claiming poor health status were retained in the analysis. We used a Generalized Linear Spatial Model (GLSM) to study the relationship between Saudi and non-Saudi household’s health status and socioeconomic factors. A Gaussian process with a powered exponential spatial correlation function was introduced on the right-hand side of the model to consider the unexplained spatial variation in the data. The statistical results show the progressive increase in the number of Saudi and non-Saudi households claiming poor health status with the high Saudi unemployment rate, low average monthly income and high current daily smokers. The results of the statistical analyses show the wider potential of GLSM for analyzing data of this kind and the important risk of misleading interpretations when the non-spatial analysis is used on spatially structured data. The method of inference was Bayesian using Markov Chain Monte Carlo Implementation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".