The factor structure of the general health questionnaire (GHQ12) in Saudi Arabia
Bibliographic record
Abstract
BACKGROUND: The General Health Questionnaire-12 (GHQ-12) is one of the most unique and extensively used self-report instruments for evaluating psychological disorders and strains. However, the factor structure of GHQ-12 has not been fully explored. The current study aims to assess the factorial structure of GHQ-12 in a large cross-sectional data-set extracted from Al Kharj central region of Saudi Arabia. METHODS: Population based cross sectional data was extracted from January 2016 to June 2016 from Al Kharj population recruiting 1019 respondents aged 18 and above. Exploratory factor analysis (EFA) was applied together with multiple regression analysis to extract and retain factors. Mean GHQ-12 score for demographic and health-related traits were used for assessing this association. Statistical analysis was carried out using STATA version 12.1. RESULTS: Three factors, including social dysfunction, anxiety, and loss of confidence were extracted from the factor structure. 55% of the overall variance was obtained through these factors. Total score of GHQ-12 ranged from 0 to 32 with a mean score of 12. CONCLUSION: Investigation of the factor structure of GHQ-12 demonstrated that GHQ-12 is a good measure for evaluating the general health of Saudi population. Future studies based on a larger sample size of non-clinical respondents will be useful to evaluate the practical effectiveness of GHQ-12 factors.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".