Measurement issues related to the evaluation and monitoring of major depression prevalence in Canada.
Bibliographic record
Abstract
Monitoring major depression prevalence is important because of the substantial impact of this condition on population health. Local or regional surveys using cost-efficient methods (e.g. data collection by telephone interview) may provide useful epidemiological data, as may the inclusion of brief diagnostic modules for major depression in general health surveys. In Canada, the Composite International Diagnostic Interview Short Form for Major Depression (CIDI-SFMD) has been widely employed for both purposes. The recent Canadian Community Health Survey 1.2 (2002), which employed a more detailed diagnostic interview (the World Mental Health 2000 CIDI), provides a standard against which to evaluate the performance of the CIDI-SFMD. A tendency to at times overestimate prevalence appears to be a characteristic of the CIDI-SFMD, and it has produced a broad range of prevalence estimates, suggesting a greater vulnerability to study-specific or contextual factors. However, the pattern of association of major depression with potential demographic determinants is not consistent with the classical "dilution" effect expected to occur with non-differential misclassification bias.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.073 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".