University is Not for the Weak: Student Communication of Mental Health on Twitter
Bibliographic record
Abstract
Well before the COVID-19 pandemic exacerbated postsecondary students' mental health challenges, students were already struggling with issues that were pervasive in higher education, including anxiety, depression, overwhelm, burnout, and difficulty accessing mental health supports. This paper examines 1007 Twitter posts pertaining to higher education students' mental health between February 2019 and March 2021. Students expressed feelings that their institutions did not care about sound mental health and that higher education is an environment primed for anxiety and depression. Students also expressed a desire for timely, online counselling and closer contact and communication with their instructors. Online/virtual therapy/counselling was particularly valuable for students, and they appreciated accommodations that faculty made for them during the pandemic. Students also used Twitter to offer support and encouragement to one another. This study has implications for pedagogical developments and revisions to mental health supports available to college and university students in both online and face-to-face environments.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".