Psychological Wellbeing of Diabetic Individuals, Prediabetics, and Non-diabetics: A Population-Based Study in Saudi Arabia
Bibliographic record
Abstract
Background The increased burden of diabetes affects the quality of life, including psychosocial problems. The study aims to compare the psychological well-being of individuals who are prediabetic, diabetic, or non-diabetic. Methods A cross-sectional exploratory study was conducted from January to June 2016 (n = 1,019) in Al Kharj, Saudi Arabia. After consent and questionnaires were filled out, trained staff took blood samples followed by anthropometry. Chi-squared tests, one-way ANOVA, and multiple linear regression analyses were conducted to examine the association between diabetes classes defined by HbA1c cut-off levels set by the American Diabetes Association (three categories), individual items, and total score in general health questionnaire (GHQ). An ROC curve was plotted for the total GHQ-12 score against HbA1c. Findings The mean GHQ score for psychological distress was significantly higher (F = 6.569, P = 0.038) in the diabetics (mean = 14.7) and the prediabetics (12.4) than in the non-diabetics (10.71). Four out of six positive GHQ items and three out of six negative GHQ items significantly differed among the three classes of diabetes. The adjusted multivariate analysis revealed that people with diabetes were most likely to report psychological distress compared to non-diabetics (unstandardized beta = 2.414; P = 0.037). The AUC examining the relationship between HBA1c and GHQ scores showed a moderate but statistically insignificant sensitivity/specificity of 0.643 (P = 0.23). Conclusion This study demonstrates that psychological wellbeing is substantially poorer among diabetic or prediabetic individuals than non-diabetic individuals. Future longitudinal studies are required to examine a plausible causal relationship between diabetes/prediabetes and psychological distress.
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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 | 0.001 | 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) | 0.001 | 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".