Trends in Post-Secondary Student Stress: A Pan-Canadian Study
Bibliographic record
Abstract
Objective Previous research has evaluated the sources of post-secondary student stress, but has failed to explore whether stressors fluctuate over time. The purpose of this research was to use the Post-Secondary Student Stressors Index to examine whether stressors changed significantly and meaningfully over the course of an academic year. Due to the timing of data collection, results also provide context around students’ experiences of stress during the COVID-19 pandemic. Method Cross-sectional data was collected at 3 time points via online surveys over the course of the 2020–2021 academic year from >10,000 students. Participants attended 15 post-secondary institutions across Canada, representing 9 provinces and 1 territory. Validated instruments were used to assess levels of stress, distress and the severity of student-specific stressors. Kruskal–Wallis ranked tests and multiple pairwise comparison analyses were conducted to assess whether the mean severity of stressors changed over time. Standard effect sizes were calculated using Cohen's d. Results Mean levels of stress and psychological distress were high at the start of the study and remained high across time points. A similarly high level of stress was observed on average for student-specific stressors. While significant differences in mean severity were observed over time for some stressors, standardized effect sizes were negligible, suggesting little meaningful change and consistent levels of chronic stress over the course of the academic year. Conclusions This is the first paper to examine trends in student-specific stress using a nationwide sample of Canadian post-secondary students during the first year of the COVID-19 pandemic. Patterns observed in student-specific stressors reflected changes likely to be indicative of the pandemic, including the most severe stress associated with academics, finances and concerns for the future. Implications for future research are discussed, in particular, the importance of examining stressors related to COVID-19 and their impact on student mental health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".