Student Mental Wellbeing Interventions with a Second-Year Engineering Cohort
Bibliographic record
Abstract
Concern about student mental wellbeing in university students is high, and first-and second-year students in particular have shown higher levels of depression, anxiety and stress. Evidence indicates that there is a value in context specific mental health literacy interventions embedded into regular academic classes, and that coping strategies help students to not only deal with stress but also to improve cognitive motivation and academic achievement. Within our second-year mechanical engineering academic program, we implemented an embedded series of interventions with the goal of helping students develop and use coping strategies in the face of stress. We found that students could describe their own stress response and coping strategies, and over 80% expected content on stress response and coping strategies would be useful in their own lives. About half of students viewed the interventions as beneficial to them. A third of students expected the activities would positively impact their academics, while another third felt they would have no impact. Test anxiety increased over the year, which we felt was a reasonable result given the rigour of the program. Since we did not have a control group, we could not determine whether our intervention led to lower levels of test anxiety than would have otherwise occurred. Our results suggest that academic buoyancy (day-to-day resilience in an academic program) is a critical skill - low academic buoyancy was a significant predictor of increased test anxiety, while low self-efficacy was not. This finding, along with students responses indicating the usefulness of the interventions, support our approach of introducing coping skills into an academic engineering program.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".