Should Psychological Distress Screening in the Community Account for Self-Perceived Health Status?
Bibliographic record
Abstract
OBJECTIVE: Psychological distress questionnaires are often used as screening instruments for mental disorders in clinical and epidemiologic settings. Poor physical health may affect the screening properties of a questionnaire. We evaluate the effect of self-perceived health status on the screening performance of the Kessler K10 and K6 scales in a community sample. METHODS: We used data from the Canadian Community Health Survey: Mental Health and Well-Being (CCHS 1.2). Psychological distress was measured by the 6-item (K6) and the 10-item (K10) Kessler instrument. Depression and anxiety disorders were assessed using the World Mental Health Composite International Diagnostic Interview (1-month estimates). Optimal cut-off points regarding health status were determined by finding the K6 and K10 values that allowed for the best balance between sensitivity and specificity. Stratum-specific likelihood ratios (SSLRs) were computed to define strata with discriminating power. RESULTS: There was a strong association between the screening performance of the K6 and K10 scales and self-perceived health status: for the K10 scale, a cut-off point of 5/6 yielded the best balance between sensitivity and specificity for subjects with excellent or very good health status, while a cut-off point of 14/15 yielded the best balance between sensitivity and specificity for subjects with poor health status. CONCLUSIONS: The combination of the K6 and K10 scales, with a self-rated health status item, may improve screening properties of the 2 scales.
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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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".