Strengths of associations between depressive symptoms and loneliness, perfectionistic concerns, risky alcohol use and physical activity across levels of sleep quality in Swedish university students: A cross‐sectional study
Bibliographic record
Abstract
Summary Previous research shows that sleep quality may interact with some other predictors of depression, such that poor sleep could strengthen the association between these factors and depression. We aimed to determine the presence of statistical interactions between sleep quality and loneliness, risky alcohol use, perfectionistic concerns and/or physical inactivity in relation to depressive symptoms. Further, we aimed to describe the functional form of the statistical interactions and associations. We used a cross‐sectional design and included 4262 Swedish university students. All measures were self‐reported, sleep quality was measured with the Pittsburgh Sleep Quality Index, and depressive symptoms with the short‐form Depression, Anxiety and Stress Scale. Regression models of increasing complexity (linear and non‐linear, with and without interactions) were compared to determine the presence of associations and statistical interactions, and to explore the best functional form for these associations and interactions. Out‐of‐sample R2 from repeated cross‐validation was used to select the final models. We found that sleep quality was associated with depressive symptoms in all final models. Sleep quality showed a linear interaction with perfectionistic concerns in relation to depressive symptoms, such that perfectionistic concerns were more strongly associated with depressive symptoms when sleep quality was poor. Loneliness, risky alcohol use and physical inactivity were non‐linearly associated with depressive symptoms but did not interact with sleep quality. We concluded that out of the four examined variables, only perfectionistic concerns interacted with sleep quality in relation to depressive symptoms. This interaction was weak and explained little of the overall variance in depressive symptoms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".