Evaluation of 2 Measures of Psychological Distress as Screeners for Depression in the General Population
Bibliographic record
Abstract
OBJECTIVE: Structured diagnostic interviews are very time-consuming and therefore increase both the expense and the respondent burden in epidemiologic surveys. A 2-staged interview that screens potential cases before the full diagnostic instrument is administered has the potential to greatly reduce the average interview length. In this paper, we evaluate 2 measures of psychological distress (the Kessler 6- and 10-Item Psychological Distress Scales [K6 and K10]) as potential screening instruments for depression. METHODS: We use data from Cycle 1.2 of the Canadian Community Health Survey and receiver operator characteristic analysis to examine the agreement between the K6 and K10 and the World Mental Health Composite International Diagnostic Interview module for major depression (1-month and 12-month estimates). RESULTS: Of the respondents, 823 were positive for 1-month depression (2.0%; 95% confidence interval [CI], 1.8% to 2.2%), and 1930 were positive for 12-month depression (4.8%; 95%CI, 4.5% to 5.1%). Both the K6 and K10 performed very well as predictors of 1-month depression, with areas under the curve (AUC) of 0.929 (95%CI, 0.908 to 0.949) for the K10 and 0.926 (95%CI, 0.905 to 0.947) for the K6. For 12-month depression, the AUCs remained good at 0.866 (95%CI, 0.848 to 0.883) for the K10 and 0.858 (95%CI, 0.840 to 0.876) for the K6. CONCLUSIONS: Both the K6 and the K10 appear to be excellent screening instruments, especially for current depression. Although performance of the 2 instruments is similar, the K6 is more attractive for use as a screening instrument because of the lower response burden.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".