Small Signal, Big Noise: Performance of the CIDI Depression Module
Bibliographic record
Abstract
OBJECTIVES: With the release of data from the Canadian Community Health Survey: Mental Health and Well-Being (Cycle 1.2), researchers have, for the first time, information on several psychiatric disorders from a nationally representative sample of Canadians residing in households. This survey used the Composite International Diagnostic Interview (CIDI) to identify persons with one or more psychiatric disorders. In this paper, our primary purpose was to evaluate the evidence supporting the validity of the CIDI--that is, the extent to which the depression diagnoses generated by the CIDI reflect true cases of depression. METHOD: We conducted a critical review of the CIDI, focusing on the depression module. RESULTS: Reliability studies indicate that the CIDI performs reliably, as measured by interrater reliability. However, the use of different versions of the CIDI and the occasional exclusion of the Depression module from studies suggest that the reliability of the CIDI Depression module remains unconfirmed. The most critical issue in regard to the CIDI's performance is that clinical samples are used to test validity. A clinical sample has a higher prevalence of depression than a community sample. CONCLUSION: The results generated by the CIDI in a community setting likely will have a high false-positive rate, resulting in a falsely elevated prevalence rate. Given the widespread application of the CIDI internationally, addressing the outstanding concerns about validity with proper validation studies should become an international priority.
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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 | 0.133 | 0.333 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".