Student Learners and Preferred Agents in Mental Health-Seeking: Assessing Higher Educational Services in a Pilot Study
Bibliographic record
Abstract
ABSTRACT The prevalence of mental health problems among student learners has increased in recent decades. University and college expectations, plus the requirement for effective time management (among other things), may significantly augment this problem. School assessment practices and learning demands may give rise to anxiety and depression within the student body, ultimately affecting both their psychological and academic outcomes. We considered the agencies that students typically approached in various daily situations (e.g., where do students turn with issues related to time management or personal adjustment and anxiety – Google, parents, best friends, professors, on-campus counseling centers). A sample of 103 students from the University of Windsor in Southwestern Ontario Canada indicated which of 15 agencies they would consult, should they encounter each of 50 scenarios related to broader categories (14 in total) such as death, school, time constraints, relationship, sexual harassment, etc. The non-use of Google and the under-utilization of the peer support and student counseling centers are discussed, as are the implications for university and college administrations' consideration of student mental health issues.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".