Pre-Service Teachers Field Test a Mental Health Literacy Intervention for Grade 12 Students – The Implementation and Evaluation of Know Before You Go in Two Rural High Schools in Atlantic Canada
Bibliographic record
Abstract
Postsecondary educational life can be a challenge and an opportunity, especially for first-year students. This part of the life span is associated with the onset of many of the major mental disorders, including Depression, Psychosis, and Anxiety Disorders. A frugal, convenient and engaging upstream intervention for high school students may be needed to help better prepare them for life after high school. Know Before You Go (KBYG) is a life skills resource with embedded mental health literacy created for Grade 12 students. It is currently approved as a curriculum document in a province in Atlantic Canada. Pre-service teachers created a board game and seminar to accompany KBYG. They assisted in field testing this resource to 212 Grade 12 students at two rural high schools in Atlantic Canada. We increased the grade 12 students’ knowledge of mental health and, their perceived sense of preparedness for postsecondary education. They regarded KBYG as being of benefit to them and, they were likely to recommended KBYG to their peers. In our single-paper session, we will provide further details on the intervention format, the content within the intervention and the specific data analysis plan we used as well as the results of that analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".