Examining co-patterns of depression and alcohol misuse in emerging adults following university graduation
Bibliographic record
Abstract
Depression and alcohol use disorders are highly comorbid. Typically, alcohol use peaks in emerging adulthood (e.g., during university), and many people also develop depression at this time. Self-medication theory predicts that depressed emerging adults drink to reduce negative emotions. While research shows that depression predicts alcohol use and related problems in undergraduates, far less is known about the continuity of this association after university. Most emerging adults “mature out” of heavy drinking; however, some do not and go on to develop an alcohol use disorder. Depressed emerging adults may continue to drink heavily to cope with the stressful (e.g., remaining unemployed) transition out of university. Accordingly, using parallel process latent class growth modelling, we aimed to distinguish high- from low-risk groups of individuals based on joint patterns of depression and alcohol misuse following university graduation. Participants (N = 123) completed self-reports at three-month intervals for the year post-graduation. Results supported four classes: class 1: low stable depression and low decreasing alcohol misuse (n = 52), class 2: moderate stable depression and moderate stable alcohol misuse (n = 35), class 3: high stable depression and low stable alcohol misuse (n = 29), and class 4: high stable depression and high stable alcohol misuse (n = 8). Our findings show that the co-development of depression and alcohol misuse after university is not uniform. Most emerging adults in our sample continued to struggle with significant depressive symptoms after university, though only two classes continued to drink at moderate (class 2) and high (class 4) risk levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".