Social cohesion, depression, and substance use severity among young men: Cross-sectional and longitudinal analyses from a Swiss cohort
Bibliographic record
Abstract
INTRODUCTION: Social cohesion, depression, and problematic substance use are intertwined and poorly understood.This study aimed to examine cross-sectional and longitudinal associations between social cohesion, depression and problematic substance use amongyoung men, age 21-25. METHODS: , 2016-2018) data from the on-going Swiss CohortStudy on Substance Use Risk Factors (C-SURF), assessing social cohesion, depression, and severity of alcohol, nicotine and cannabis use during both waves. Structural Equation Models (SEMs) were employed to examine pathways in both waves under the framework of longitudinal analysis. RESULTS: Social cohesion was directly associated with depression and problem nicotine and cannabis use and indirectly associated with problem alcohol, nicotine and cannabis use through depression at both t1 and t2. Social cohesion exerted direct effects on nicotine use and cannabis use severity, but not on alcohol use severity. Social cohesion had indirect effects on problem use of all three substances, mediated via depression. The predictive direction was from depression to substance use, rather than vice versa. CONCLUSIONS: Higher social cohesion at an early age appears to protect young males from depression and problematic substance use later in life. However, once problematic substance use is established, the direct effect of social cohesion diminishes and is mediated through personal depression. Therefore, promoting a more cohesiveneighborhood in childhood or adolescenthood could play an important role preventing depression and more severe substance use behaviors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".