One Week, Many Ripples: Measuring the Impacts of the Fall Reading Week on Student Stress
Bibliographic record
Abstract
More and more Canadian post-secondary institutions are introducing a fall break into their term calendars. In 2015, a full week fall break was introduced at our university in order to enhance academic performance and improve mental health amongst students. Our interdisciplinary team surveyed undergraduate students at our university about their experience of the fall break, collected standardized measures of experienced stressors and perceptions of stress before and after the break, and hosted several focus groups to develop a detailed narrative of students’ experience. Stress can also be assessed through non-invasive hormone measures. We collected saliva samples to profile metabolic hormones, cortisol, and dehydroepiandrosterone (DHEA), from first-year male engineering students in order to document possible changes in their stress levels before and after the week-long break. This group was compared to male engineering students at a similar university that does not hold a fall break. Students exhibited a lower ratio of cortisol to DHEA after a fall break than those that did not experience a break. Our survey results indicate that the majority of students thought the fall break was a positive experience. However, self-reports of stress show a more complex picture, with many students reporting increased perceived stress after the break. Additionally, a portion of students reported that the fall break was a negative experience. To the best of our knowledge, our study is the first of its kind to use a mixed-methods approach to examine the impacts of a fall break.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.004 | 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".