Supporting student mental health while enhancing self-care: Evaluating the efficacy of a Graduate Teaching Assistant training module
Bibliographic record
Abstract
Given the unique proximity and approachability of graduate teaching assistants (GTAs) to students, training GTAs to support student mental health is critical. However, GTAs play dual roles as educators and students, who face their own stress and mental health challenges. This study examined the efficacy of an online module for GTAs focused on how to offer support to students while considering their own self-care. Using an online survey, GTAs’ beliefs (feelings of preparedness, and sense of responsibility) and responses (supportive behaviors) to scenarios of students in distress were examined. Participants also completed a measure of self-care. Compared with a general sample of GTAs who had not participated in the module (n = 111), module participants (n = 42) had higher intentions, felt more responsibility, and felt more prepared to support students in distress. They also reported higher levels of self-care. This study shows training can not only be effective at enhancing GTAs’ ability to support undergraduate student mental health but also positively impact their own self-care.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".