The Sensitivity of the K6 as a Screen for Any Disorder in Community Mental Health Surveys: A Cautionary Note
Bibliographic record
Abstract
OBJECTIVE: Short screening instruments, which exclude respondents unlikely to have psychiatric disorders, can make epidemiologic surveys shorter and more cost-effective. The Kessler 6-Item Psychological Distress Scale (K6), a measure of generalized distress, has been proposed for this role and has shown good agreement with the Composite International Diagnostic Interview (CIDI). However, performance of the K6 may vary for individual disorders or combinations of disorders. In this report, we examine the ability of the K6 to detect disorders among respondents in different diagnostic categories. METHOD: We used data from Cycle 1.2 of the Canadian Community Health Survey to assemble 5 groups of respondents with different 12-month psychiatric disorders (n = 4481). A sixth group comprised those with 2 or more disorders. We examined the sensitivity of the K6 among respondents with an individual disorder as well as those with multiple disorders. RESULTS: The sensitivity of the K6 varies significantly by disorder; it is highest among respondents with multiple disorders and lowest among those with agoraphobia only. CONCLUSIONS: Use of the K6 as a screen for the CIDI is likely to result in biased prevalence estimates. However, both instruments should be compared with a third standard to fully assess the benefits and drawbacks of their combination.
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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 | 0.331 | 0.578 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".