Studying the incidence of depression: an ‘interval’ effect
Bibliographic record
Abstract
Abstract A review of studies about the incidence of depression suggested that the length of the ‘interval’ of follow up may influence the findings. Exploration of these issues is carried out using data from the Stirling County Study, an investigation of psychiatric epidemiology in a general population. The study's customary method of diagnosis, DePression and AnXiety (DPAX), and the Diagnostic Interview Schedule (DIS) were used in an incidence investigation whose ‘interval’ was less than three years. Average annual incidence rates of depression for both DPAX and DIS were about 15 per 1000. Where longer intervals were used in the Stirling Study, rates were close to four per 1000. Projected lifetime risk based on the lower rates was more congruent with reported lifetime prevalence than that based on the higher rates. Irrespective of method, 90% or more of the incident cases gave an onset that predated the initial interview, suggesting poor reliability. This was often due to the fact that information given in the first interview met some but not all of the criteria for diagnosis. Being in the ‘borderline’ category at the beginning of the study significantly increased incidence. Thus, evidence from the Stirling County Study replicated findings that suggest an ‘interval effect’ and pointed to the need in incidence studies for distinguishing between the onset of the prodrome and the onset of diagnosable depression. Copyright © 2000 Whurr Publishers Ltd.
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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 | 0.063 | 0.211 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".