Examining the Predictors of Mental Health Outcomes Among Undergraduate Postsecondary Students in Canada
Bibliographic record
Abstract
Symptoms consistent with mental illnesses such as anxiety and depression are dominant in both prevalence and in severity among North American post-secondary student populations over the past several years. This study examines undergraduate students’ self-reported symptoms consistent with two common mental illnesses in a Canadian context, and sheds light on several predictors of students’ mental health outcomes, including perceived contextual stressors, coping strategies, and perceived barriers to help seeking. Data for this investigation were obtained through the completion of self-administered questionnaires from a sample of 209 undergraduate students attending a public western Canadian university during the fall semester of 2014. Consistent with previous research completed among post-secondary populations, a considerable proportion of students self-reported symptoms consistent with anxiety and depression. The following variables made unique contributions to the prediction of the severity of students’ self-reported symptoms: living arrangement; contextual stressors, such as social/environmental maladjustment, academic achievement, curriculum and academic expectations, time/balance, and financial stressors; styles of coping, including functional/adaptive coping, mental and behavioral disengagement, and substance abuse; and perceived barriers to treatment, including fear of self-discovery and fear of therapy. The implications of these findings for future research and intervention at the post-secondary level are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".