Educator-Informed Development of a Mental Health Literacy Course for School Staff: Classroom Well-Being Information and Strategies for Educators (Classroom WISE)
Bibliographic record
Abstract
Educators play a critical role in promoting mental health and well-being with their students. Educators also recognize that they lack knowledge and relevant learning opportunities that would allow them to feel competent in supporting student mental health. As such, educators require resources and training to allow them to develop skills in this area. The Mental Health Technology Transfer Center (MHTTC) Network partnered with the National Center for School Mental Health at the University of Maryland School of Medicine to develop Classroom Well-Being and Information for Educators (WISE), a free, three-part mental health literacy training package for educators and school staff that includes an online course, video library, and resource collection. The Classroom WISE curriculum focuses on promoting positive mental health in the classroom, as well as strategies for recognizing and responding to students experiencing mental health related distress. This paper describes the curriculum development process, including results of focus groups and key informant interviews with educators and school mental health experts. Adoption of Classroom WISE can help educators support student mental health and assist in ameliorating the youth mental health crisis.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".