Identifying key areas of post-secondary student stress: Principal component analysis of the Post-Secondary Student Stressors Index (PSSI)
Bibliographic record
Abstract
Stress and mental health problems are prominent in Canadian postsecondary populations. Experiences of stress vary widely across the country due to student differences. The Post-Secondary Student Stressors Index (PSSI) is a 46-item inventory that assesses student-specific stressors. The PSSI has previously been validated in Ontario and Canada more broadly. However, as a new scale, the PSSI requires validation across different contexts. The purpose of this study was to use the PSSI to determine the areas of stress specific to the Manitoba population, utilizing more detailed information on severity and frequency of stress. Data were drawn from a Manitoba subset of the PSSI respondents. This resulted in a sample size of 2856 students from the University of Manitoba. Each item on the PSSI-46 was transformed into a new variable representing the inclusion of the frequency and severity variables for each stressor. Principal component analysis (PCA) was used to explore relationships between the 46 items of the PSSI. Direct oblimin rotation method was used to examine fit indicators. Spearman's rho was used to examine correlations between the revised PSSI score and those on other instruments. Cronbach's alpha was used to determine the internal reliability. The PCA produced 10 stress components consisting of 40 items. Six items did not load onto any components and were therefore excluded from component formation. The components demonstrated strong psychometric properties and internal validity. This study utilized the PSSI measure in a novel way, both through context and statistical assessment. These components of stress may be employed in future research that assesses post-secondary student stress.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".