Investigating Possible Reciprocal Relations Between Depressive and Problem Gambling Symptoms in Emerging Adults
Bibliographic record
Abstract
OBJECTIVE: Previous cross-sectional research has shown that depression and problem gambling co-occur. Longitudinal research, however, allows for a better determination of directionality, as behavioural changes in gambling involvement can be more reliably studied over time. Our study assesses symptoms of depression and problem gambling across 4 waves and addresses whether their relation is directional (with one reliably preceding the other), bidirectional, or pathoplastic. METHOD: As part of the Manitoba Longitudinal Study of Young Adults, prospective data were collected on Canadian young adults' (Wave 1: n = 679, 51.8% female, aged 18 to 20 years) depressive symptoms, involvement in gambling, and risky gambling behaviour. Recruitment and the first cycle of data collection (Wave 1) took place in fall 2007. Three additional waves of data collection then occurred in 12- to 18-month intervals: fall 2008, spring 2010, and spring 2011. The Problem Gambling Severity Index and the Composite International Diagnostic Interview-Short Form were administered through telephone interview at each wave. RESULTS: Bivariate growth curves showed that depressive and problem gambling symptoms were positively correlated at Wave 1, Wave 2, and Wave 4. Neither disorder was found to be a risk factor for the other, and depression and problem gambling were not pathoplastically related (that is, increases in one did not result in increases in the other over time, and vice versa). CONCLUSIONS: While depression and problem gambling are related, their co-occurrence may be better explained not by depressive- or gambling-related risk, but by the presence of a common underlying factor (such as substance abuse).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".