A Screening Instrument for Psychological Distress in Botswana: Validation of the Setswana Version of the 28-Item General Health Questionnaire
Bibliographic record
Abstract
OBJECTIVES: To develop a Setswana version of the 28-item General Health Questionnaire (GHQ-28) for use in Botswana. METHODS: A sample of 126 subjects attending primary healthcare clinics completed the GHQ-28, which contains four subscales of seven questions each for the following domains: somatic concerns, anxiety, social function and depression. All subjects were also interviewed with the Clinical Interview Schedule (CIS). Psychiatric casesness was ascertained by CIS scores greater than 20 and an overall severity of symptoms rating (OSR) of > or =2 on a 0-4 point scale. A receiver operating characteristic (ROC) analysis was undertaken to assess which GHQ cut-off score gave the best casesness yield as defined by the combined CIS and OSR assessments. RESULTS: Of the 126 subjects enrolled, 122 completed the study, with 18 (14.5%) meeting criteria for caseness. There were no gender differences with respect to GHQ or CIS scores. The ROC analysis revealed that the GHQ threshold of 7/8 gave the best sensitivity (88%) and specificity (67%) results. The internal consistency of the translated GHQ was maintained with Cronbach alpha scores ranging from 0.76 to 0.91 for the subscales. CONCLUSIONS: The Setswana GHQ-28 represents a valid instrument of screening for psychological distress in a primary healthcare setting in Botswana.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".