Baseline income, follow-up income, income mobility and their roles in mental disorders: a longitudinal intra-generational community-based study
Bibliographic record
Abstract
BACKGROUND: Although a number of studies have found that income mobility associated with an elevated risk of mental disorders, existing research does not provide sufficient evidence of how exactly individuals' experience of income mobility per se affects their risk of mental health outcomes. This present study aimed to explore roles of baseline income, follow-up income, and income mobility in the development of mental disorders using an intra-generational, longitudinal follow-up study. METHODS: We used data from the Montreal South-West Longitudinal Catchment Area Study. A total of 1117 participants with complete information both on income and past 12-month diagnoses of mental disorders were selected for this study. Diagonal Reference Models were used to simultaneously examine roles of income at baseline, income at follow-up, and income mobility in mental disorders during a 4-year follow-up. RESULTS: Both baseline and follow-up income were important predictors for any mental disorder and major depression among males and females. Those with low income had a higher risk of any mental disorders and major depression. No evidence was found to support an association between income mobility (neither downwards nor upwards) and mental disorders. Marital status was uniquely associated with any mental disorder among males. Having a pre-existing diagnosis of any mental disorder at origin was associated with any mental disorder and major depression at the end of the 4-year follow-up. CONCLUSIONS: This study first simultaneously examined roles of income at baseline, at follow-up, and mobility in mental disorders among a large-scale intra-generational community-based study. This present study provides additional evidence on how income is associated with an individuals' likelihood of mental disorders.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".