Long‐Term Health Implications of Students’ Friendship Formation during the Transition to University
Bibliographic record
Abstract
BACKGROUND: The transition to university is a major life change wherein young adults' primary support system shifts from the family to peers. Can change in social integration (operationalised as number of friends) during the first term at university contribute to students' health years later, and if so, how? METHODS: The friendship formation of 67 students at a large Canadian university was assessed during their first term. These data were used to predict self-reported health and health behaviors (physical exercise, diet, tobacco, alcohol and marijuana consumption) at a follow-up assessment that occurred near the end of their time at university (2 or 3 years later). RESULTS: Linear regression models showed that students who made more friends in their first term reported better health and a healthier diet at the follow-up (2 or 3 years later). Perceived social support at the follow-up mediated the relationship between friendship formation and self-reported health but not diet. CONCLUSIONS: This study provides evidence for both (1) an indirect effect of friendship formation on self-reported health via perceived social support, and (2) a direct effect of friendship formation on a healthy diet. Broadly, these results highlight the importance of friendship formation and social integration for the long-term well-being of university students.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".