Reciprocal Effects of Social Support in Major Depression Epidemiology
Bibliographic record
Abstract
BACKGROUND: The clinical course and epidemiology of major depressive episodes (MDEs) may be influenced by reciprocal interactions between an individual and the social environment. Epidemiological data concerning these interactions may assist with anticipating the clinical needs of depressed patients. METHODS: The data source for this study was a Canadian longitudinal study, the National Population Health Survey (NPHS), which provided 8 years of follow-up data. The NPHS interview included a brief diagnostic indicator for MDE, the Composite International Diagnostic Interview Short Form for Major Depression (CIDI-SFMD). The NPHS interview also incorporated the Medical Outcomes Study Social Support Scale (MOSSS) and a set of relevant demographic and health-related measures. The MOSSS assesses total social support and four specific dimensions of social support. Hazard ratios (HR) were used to quantify associations in the longitudinal data. RESULTS: LOWER QUARTILE TOTAL SOCIAL SUPPORT RATINGS PREDICTED MDE INCIDENCE: the HR adjusted for age and sex was 1.9 (95% CI 1.6 - 2.2). Lower quartile ratings in specific social support dimensions yielded similar HRs. MDE was associated with emergence of lower-quartile affection social support (age and sex adjusted HR 1.3, 95% CI 1.1 - 1.7), but other aspects of social support were not consistently associated with MDE. CONCLUSIONS: Low social support appears to be a robust risk factor for MDE and can be used to identify persons at higher risk of MDE. Evidence that MDE has a negative effect on social support was weaker and was restricted to affection social support.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".