A Scoping Review and Psychometric Assessment of Survey Instruments Used to Evaluate Post-secondary Student Stress
Bibliographic record
Abstract
Post-secondary students are exposed to a wide variety of stressors, and mental health-related concerns continue to increase among this population. The development and improvement of mental health services hinges on the use of valid and reliable measurement tools to assess student stress and evaluate the efficacy of interventions. To date, there has been no comprehensive overview of available instruments intended to evaluate post-secondary student stress. The purpose of this study was to conduct a rigorous scoping review of published works detailing the use of instruments to evaluate post-secondary student stress, and evaluate the psychometric properties of these tools as well as their suitability for use among post-secondary populations. Four academic databases were searched, including HaPI, CINAHL, Medline, and PsycINFO, including peer-reviewed records published in English between 2012 and 2024. Articles were independently screened for inclusion by title, abstract, and full-text review. A total of 62 records and 11 instruments were screened into the review. The majority of instruments included in our review did not meet the criteria for suitability, nor did they have published, comprehensive psychometric evaluations. The Post-Secondary Student Stressors Index emerged as the most appropriate instrument for evaluating post-secondary student-specific stress owing to both its suitability and strong psychometric properties. The Perceived Stress Scale and Stress Overload Scale were deemed to be the most appropriate instruments for evaluating general stress levels.
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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 | 0.056 | 0.227 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.050 | 0.049 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".