Educators’ lived experiences of encountering and supporting the mental wellness of university students
Bibliographic record
Abstract
Globally, there is an increasing prevalence of higher education students with mental health issues. Educators are guaranteed contact points, and students often seek their support to manage their mental wellness. However, there is limited research describing educators’ experiences of these interactions. This interpretive phenomenological study engaged 16 educators from an institution in Canada and Australia. Interviews were conducted to understand their lived experiences interacting with and supporting students with mental health issues. Data demonstrated that educators encountered students with challenges to mental wellness, most commonly around course assessments. These encounters caused strain on educators personally and professionally, which they responded to by adapting teaching practices to mitigate risks to student and educator wellbeing, compartmentalization and boundaries, and relational connection and support. Over time they evolved to manage this and incorporate this role into their teaching, with more confidence and less impact on themselves. This manuscript highlights the lived experiences of educators engaging with students with mental health stressors, and provides tangible examples of professional and personal modifications that mitigated the strain on the educator caused by these encounters.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| 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".