Lonely but not alone: Examining correlates of loneliness among Canadian post-secondary students
Bibliographic record
Abstract
Objective: Loneliness is increasingly acknowledged as a public health concern due to its association with morbidity and mortality. The prevalence of loneliness is highest in the post-secondary population. Understanding the correlates of loneliness may assist in developing policy and program interventions. Participants and Methods: Post-secondary students (n = 28,975) from the Winter 2022 Canadian Campus Wellbeing Survey (CCWS) cycle. A multi-level logistic regression controlling for the institution was built to determine how demographic, health behaviors, mental health and institutional level factors are associated with loneliness. Results: The prevalence of loneliness was 31% in our sample. Demographic (e.g., gender, sexual orientation, social economic status), health behaviors (e.g., physical activity and substance use), mental health (e.g., mental distress and social support) and institutional factors (e.g., college or university institution) impacted the odds of reporting loneliness (p < 0.05). Conclusion: Our findings suggest loneliness might require greater attention by institutional staff and administrators.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".