Dimensionality of the Swahili version of the General Health Questionnaire (GHQ-12) in a Kenyan population: A confirmatory factor analysis
Bibliographic record
Abstract
The current study evaluated the Kiswahili version of General Health Questionnaire (GHQ-12) in a Kenyan context comprising of women exposed to gender-based violence. Participants were randomly drawn from community sampling using household screening methods in peri-urban areas in Nairobi. A total of 1,394 participants with varying levels of literacy (years of education: mean [M] = 9.42; standard deviation [SD] = 3.73) and aged between 18 and 89 years were recruited for the study. The observed factor structure of the GHQ-12 was evaluated using six most tested models querying the dimensionality of the instrument insofar as the impacts of positive and negative wording effects in driving multidimensionality. Results from the confirmatory factor analysis supported a bifactor model, consisting of a general distress factor and two separate factors representing common variance due to the positive and negative wording of items. Overall, the findings support the use of the Kiswahili version of the GHQ-12 as a unidimensional construct with method-specific variance owing to wording effects. Importantly, GHQ-12 responses from a sample of Kenyan women with relatively low levels of literacy are congruent with the factor structure observed in other cross-cultural settings in low- and-middle-income countries.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".