Lessons learned from a critical appraisal of a fall break policy in higher education: A case study
Bibliographic record
Abstract
The incidence, severity and persistence of mental health issues is increasing across post- secondary campuses (Zivin et al., 2009; Canada Newswire, 2012) with these students now viewed as a high-risk population (Stallman, 2010). Many Canadian universities are implementing a policy for a fall break in hopes of alleviating students’ stress and anxiety in order to improve mental health, heighten retention, and increase academic productivity. To date, there is limited empirical evidence to guide the development of policy and the delivery of effective practices to alleviate school-related stress and anxiety. This thesis is presented as a three paper, manuscript approach. The focus of this project was to appraise the development and implementation of a fall break and then evaluate its effectiveness in an effort to address rising concerns related to mental health for post-secondary students. Three thousand and seventy-one students in years one to four completed a post-break survey during one week in January of 2013, 2014, 2015. Of those, 1019 were male and 2052 female. Thirty-three students varying in years from one to four participated in focus groups in February of 2013, 2014, 2015. Of those 4 were male and 19 were female. Ten faculty from varying faculties and one informant participated in interviews in spring, 2018. Analyses from the surveys revealed that overall, students are in favour of having a fall break. Even though a small percentage of participants perceived their workload to go up before and after the break, 90% of students agree that the fall break was useful in reducing school related stress levels. However, the focus group, faculty and informant interviews revealed that the timing of the fall break had an impact on how students and faculty experienced the break and thus influenced perceptions on the impact that the break had on student mental health. Comprehensive evidence about whether a fall break policy supports or undermines the mental health of students needs to be assessed using a range of indicators before its implementation. This will help post-secondary institutions determine whether a break in the fall semester can be an effective approach to addressing students’ stress and anxiety.
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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 | 0.067 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.032 | 0.020 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.012 | 0.017 |
| 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".