The Importance of Mental Health Awareness Among Post-Secondary Educators: A Workshop to Promote Understanding and Competence
Bibliographic record
Abstract
Attending post-secondary education is characterised by substantial challenges and transitions (Kitzrow, 2003). Moreover, mental health problems are prevalent among post secondary students and have increased in recent years (Hunt & Eisenberg, 2010). Training designed to help educators identify mental health problems has demonstrated efficacy (Askell-Williams, & Lawson, 2013), suggesting a need for the inclusion of mental health-informed teaching in post secondary education. Participants of this workshop will receive a brief introduction to the impact of mental health problems in post-secondary settings. Participants will also partake in informational video presentations, discussions of current post-secondary policies and their impact on student mental health, as well as role-play activities targeting strategies for safe and effective intervention. Student resources are included to guide participants in role play activities. Additional resources that provide psycho-education and strategies for the classroom are presented. Please note that facilitators that do not have a background in mental health may wish to present (and/or prepare for) this workshop with someone who has expertise in this area.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".