Comparison of the Personal Health Questionnaire and the Self Reporting Questionnaire in rural Pakistan.
Bibliographic record
Abstract
OBJECTIVE: To apply ROC analysis to select the best threshold scores for the PHQ and SRQ; to compare the sensitivity and specificity of the PHQ and SRQ against a criterion diagnosis of depressive disorder in a community sample in rural Pakistan, and to examine the influence of socio-demographic factors on misclassification. METHODS: The study used a two-stage design. Receiver Operating Characteristic (ROC) analysis was used to estimate the optimal threshold score and to compare the ability of the Self Reporting Questionnaire (SRQ) and the Personal Health Questionnaire (PHQ) to discriminate between cases of depressive disorder and non-cases. RESULTS: The results of the ROC analysis suggest that the SRQ is superior to the PHQ, and at the threshold of 5/6, the SRQ has superior sensitivity, negative predictive value and percentage agreement compared with the PHQ. When the SRQ threshold is raised it gains specificity, and at a cut-off threshold of 7/8 it is superior to the PHQ (5/6) in all validity coefficients and percentage agreement. Only gender and the presence of a confidant had a significant effect on misclassification using the SRQ among the cases. Both questionnaires performed better for females based on comparison of the areas under the ROC curves. CONCLUSION: This study has demonstrated that the Urdu translations of both the PHQ and SRQ can be used as screening tests for depressive disorder in the Pakistani population. People with little or no education answer both somatic and psychological items with equal ease. Inconclusion, the PHQ does not appear to have any advantage over the SRQ.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".