Predictors of Self-Rated Health and Lifestyle Behaviours in Swedish University Students
Bibliographic record
Abstract
BACKGROUND: Lifestyle behaviours are usually formed during youth or young adulthood which makes college students a particularly vulnerable group that easily can adopt unhealthy lifestyle behaviour. AIM: The aim of this cross-sectional study was to explore the influence of socio-demographic factors on Swedish university students' lifestyle behaviours and self-rated health. METHOD: Data were collected from a convenience sample of 152 students using questionnaires consisting of a socio-demographic section followed by previously well-validated instruments. Data were analysed using descriptive statistics: t-tests, analysis of variance (ANOVA) and regression tests. FINDINGS: The results of this study show that the lifestyle behaviours under study (physical activity, perceived stress and eating behaviours) as well as self-rated health can be predicted to a certain extent by socio-demographic factors such as gender, mother tongue and parents' educational level. Male university students were shown to be physically more active than female students; the male students were less stressed and rated their overall health, fitness level and mental health higher. Female students were more prone to adopt unhealthy eating behaviours. DISCUSSION: This study addresses gender differences and their influences on lifestyle behaviours; it provides both theoretical explanations for these differences as well as presents some practical implications of the findings.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".