Reliability, validity and dimensionality of the GHQ-12 in South African populations: Structural equation modelling (SEM)
Bibliographic record
Abstract
Abstract Introduction Health Care Workers (HCWs) were among the high-risk groups for SARS-CoV-2 infection and suffer a high burden of poor mental health including depression, anxiety, traumatic stress, avoidance and burnout. The 12-Item General Health Questionnaire (GHQ-12) has showed best fit in both a one-factor structure and a multidimensional structure for the screening of common mental disorders and psychiatric well-being. The aim was to test for the reliability and validity and ascertain the factor structure of the GHQ-12 in a South African HCW population. Methods Data was collected from 832 public hospital and clinic staff during the COVID-19 pandemic in Gauteng, South Africa. The factor structure of the GHQ12 in this professional population was examined by exploratory factor analysis (EFA) to identify factors, confirmatory factor analysis (CFA) for construct validity and structural equation modelling (SEM). Results The GHQ-12 median score was higher (25) in women than in men (24), p=0.044. The determinant for the correlation matrix was=0.047, the Barlett test of sphericity was p<0.001, Chi square=2086.9 and Kaiser-Meyer-Olkin (KMO) of sampling adequacy was 0.86. The four factors identified were labelled as Social-Dysfunction (37.8%), Anxiety-Depression (35.4%) Capable (24.9%) and Self-Efficacy (22.7%). The entire sample had a Cronbach’s alpha of 0.85, with 0.69 for Social-Dysfunction, 0.74 for Anxiety-Depression, 0.64 for Capable and 0.52 for Self-Efficacy in orthogonal (varimax) factor loadings. Conclusions The GHQ-12 tool displayed adequate reliability and validity in measuring psychological distress in a professional group with a four-factor model suggesting multidimensionality in this group rather than a unidimensional construct.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".