Uneven Learning Landscapes Ahead: Instructor Perspectives on Undergraduate Student Mental Health
Bibliographic record
Abstract
This study investigates instructor perspectives on undergraduate student mental health in a mid-sized comprehensive univer-sity in southwestern Ontario. Through a survey (n = 190) and two focus group discussions (n = 8), instructors reported differ-ent perspectives toward student mental health (some inclusive, some tolerant, and some discriminatory); changing workloads and pressures associated with addressing student mental health; and a predominant framing of mental health conditions as biomedical concerns. Using the conceptual framework of learning landscapes (Noyes, 2004), I argue that students with mental health concerns experience uneven and sometimes inequitable learning environments across their post-secondary education due to the differing microclimates created by individual instructors. While institutional policies and advocacy efforts to support mental health on campus may help to shift the learning landscape, they are unlikely to change the biases exhibited by some instructors that represent barriers to accessible post-secondary education.
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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 | 0.002 | 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 | 0.002 | 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) | 0.001 | 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".