Bibliographic record
Abstract
OBJECTIVE: To estimate the 12-month period prevalence of major depression in Calgary. METHODS: Subjects (n = 2542) were selected using random digit dialing (RDD) and interviewed by telephone using the Composite International Diagnostic Interview-Short Form for Major Depression (CIDI-SFMD). A subset of this sample was recontacted and administered the full mood disorders section of the CIDI. RESULTS: The weighted proportion of the sample scoring in the positive range on the major depression predictor was 14.7%. Validation data determined that approximately three-quarters of these subjects would be expected to have major depression according to the CIDI. Hence, the estimated 12-month period prevalence of major depression was approximately 11.0%. CONCLUSIONS: This prevalence estimate is higher than most, but not all, previous Canadian estimates and resembles that of the American National Comorbidity Survey (NCS). Calgary may have a high prevalence of major depression; however, because methodologically comparable studies are not available, to conclude this would be premature. Selection bias due to the RDD sampling (and the associated relatively high rate of nonresponse) may have led to an inflated prevalence estimate. Alternatively, because it allows increased anonymity and emotional "distance" from the nonprofessional interviewers, telephone-based data collection may be more sensitive to psychopathology than face-to-face interviewing.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".