Experiencing Transition and Mental Distress: Narratives of First-Year University Students
Bibliographic record
Abstract
Student mental health and well-being has become an area of increased attention and relevance within Canadian higher education. More university students every year report mental health problems and universities have developed strategies to promote student mental health. Direct-entry first-year university students are in need of unique support for their mental well-being because they are in a critical developmental time in emerging adulthood. The purpose of this inquiry was to explore the narratives of students who experienced mental distress during their first year of university. This inquiry asked: How do direct-entry university students, who identify as having undergone mental distress in their first year, experience the transition from high school to university? I engaged in a qualitative narrative inquiry methodology. I conducted narrative interviews with eight current undergraduate students who had entered university directly from high school and had experienced mental distress during their first year of university. In my analysis, I elucidated individual and collective narratives from these students’ experiences. The participants’ experiences were divided in two subsets of narrative portraits: current first-year students and current upper-year students. The subsets were distinguished by the participants’ temporal positioning to their first-year university experience. Two collective narratives emerged: entangled transitions and waves of mental distress. Through this inquiry, the participants engaged in narrative learning to restory their experience of transition and mental distress. To support the transitional experiences of direct-entry students, universities should implement holistic approaches that frame first-year university students as whole people and emerging adult learners.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".