Households Health Status in Saudi Arabia: Spatial Distribution and Association With Socioeconomic Factors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it