An Analysis of Health Policies Designed to Control and Prevent Diabetes in Saudi Arabia
Bibliographic record
Abstract
A trend analysis of the prevalence of diabetes in Saudi Arabia revealed a steep increase in diagnosis rates for the disease between the years 1989 and 2009. Between these years, the percentage of the population suffering from diabetes rose from 10.6% to 32.1% of the adult population, and the diagnosis rate is likely to increase in the future. The controlling and prevention of diabetes in the future, therefore, would potentially benefit from a scholarly review of current policies and programmes designed to contain the disease. The current study examines such policies and programmes, specifically those existing in Saudi Arabia and which are currently in operation in 2016. It employs the thematic-content-analysis technique to review key literature, and also uses Walt and Gibson’s policy triangle framework to facilitate the analysis. Searches of PubMed and Medline databases were conducted to locate sources and sources were manually screened by the authors before inclusion in the study. The study concludes that prime obstacles to the successful implementation of diabetes programmes are: insufficient training of practitioners for the treatment of diabetes; lack of remuneration for the work of diabetes educators and no existing evaluation of their outputs; and a lack of training and appropriate modes of qualifying professionally for diabetes educators. The authors recommend that the Saudi government award a greater proportion of resources to programmes designed to treat diabetes sufferers, as well as educational programmes related to disease for the wider public.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".