Resilient minds: Strategies to enhance student transitions from secondary to post-secondary education settings
Bibliographic record
Abstract
Students transitioning from secondary to post-secondary education find the experience overwhelming. This article reports on the strategies developed and implemented to support first-year students’ transition to programs in the Faculty of Health Sciences at Ontario Tech University. The strategies developed are targeted at the development and nurturing of skills to build and strengthen student resilience. Resilience theory recommends that a holistic approach to resilience is needed that includes both health promoting strategies and strategies to address symptoms of anxiety and stress once exhibited. The strategies reported in this article address the former approach and include a graphic novel, mandala art activity, a resilience handbook and workshop. Each of the strategies have been developed and implemented. Overall, preliminary results indicate the strategies are innovative, engaging, informative and deal with real-life experiences. The next steps include the development of an evaluation plan to further evaluate the strategies. Building on the work already completed, future plans also include the development of additional strategies in partnership with partners in the community and students. Utilizing a student-centric approach will provide the opportunity to further develop strategies for first-year post-secondary students, as well as secondary students who transition between secondary educational settings, including students from military families who are called to move more often than the general population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".