The Post-Secondary Student Stressors Index (PSSI): Proof of Concept and Implications for Use
Bibliographic record
Abstract
OBJECTIVE: This study demonstrates the utility of the Post-Secondary Student Stressors Index (PSSI), an instrument designed to identify and evaluate the sources of student stress. The PSSI is comprised of 46 stressors, rated by severity and frequency, across five domains: academics, learning environment, campus culture, interpersonal, and personal.Participants: Pilot testing of the tool was conducted among n = 535 post-secondary students enrolled at an Ontario university.METHODS: Mean severity and frequency ratings were calculated for each stressor on the instrument. Results were plotted, stratifying results by sex. T-tests for differences in means across sexes were calculated for each stressor.RESULTS: Female students in this sample consistently rated nearly all stressors on the instrument as more severe than their male counterparts. Females also reported higher frequency ratings on average, indicating that they worried more often about stressors than males. Domain-specific stressors are discussed.CONCLUSIONS: The PSSI can provide post-secondary institutions with the ability to target and improve their mental health promotion and mental illness prevention efforts.
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 | 0.036 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".