The Influence of Traits of Resilience and Internalising Symptoms of Mental Health on the Transition From Secondary to Post‐Secondary in Canada
Bibliographic record
Abstract
ABSTRACT Research on the transition from high school to university supports that over 90% of students will notice their grades decrease when entering first‐year university. However, the nature of this drop remains largely unknown. The current study proposes a path analysis model to explore the mechanism by which traits of resilience (i.e., locus of control, self‐efficacy, resilience, academic resilience and academic buoyancy) may influence grade point average (GPA) changes in students transitioning from a secondary to post‐secondary institution in Canada. The influence of student mental health on these variables was also of interest. One hundred and thirty‐nine students responded to scales assessing their locus of control (LOC), self‐efficacy, resilience, academic resilience, anxiety and depression. A basic change score was computed to quantify GPA changes during this transition. The path analysis revealed that higher self‐efficacy and internal LOC predicted higher scores of academic resilience but not GPA changes. Supplementary analyses revealed anxiety and depression were the only variables significantly related to all the model variables, including academic resilience and GPA change scores. In turn, personality constructs may influence the academic resilience of students; however, mental health may significantly influence the relationships between these constructs, academic resilience and GPA changes. Considering the COVID‐19 pandemic, the current findings may highlight the increasing impact of internalising symptoms of mental health on academic performance in a remote learning setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".