Major Depression and its Association with Long-Term Medical Conditions
Bibliographic record
Abstract
OBJECTIVE: To replicate previously reported associations between major depressive episodes (MDEs) and long-term medical conditions in a Canadian community sample. METHODS: A sample of 2542 household residents was selected using random digit dialing (RDD). Data were collected by telephone interview. The Composite International Diagnostic Interview (CIDI)-Short Form for major depression (CIDI-SFMD) was used to identify MDEs occurring in the previous 12 months. Long-term medical conditions were identified by self-report. RESULTS: The prevalence of MDE was elevated in those subjects who reported 1 or more long-term medical conditions. The association was not due to confounding by age, sex, social support, or stressful recent life events. CONCLUSION: This study replicates a previously reported association between depressive disorders and long-term medical conditions. These cross-sectional associations suggest that medical conditions may increase the risk of major depression or that major depression may increase the risk of medical conditions. Alternatively, comorbid medical conditions may influence the duration of depressive episodes, or vice versa. These explanations are not mutually exclusive.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".