Embedding Authentic and Effective Awareness About Mental Health in Pre-Service Teacher Training
Bibliographic record
Abstract
The chapter examines the urgent need for pre-service teacher training programs to integrate content on mental health. In the current neo-liberal context, there is increasing pressure on universities to streamline and shorten these programs, when in fact there might be a need to add content to their existing structure. Developing pre-service teachers' awareness around student mental health is a pressing need but one campuses are usually reluctant to address when it may represent a widening of their scope. The chapter analyzes phenomenological data collected by the author around his lived experience of delivering a course on mental health within a Canadian pre-service teacher training program. It examines the complex, rich, and diverse outcomes that are achieved (1) on teacher candidates' approaches to inclusion, (2) on their ability to navigate their own mental health issues, and (3) more widely on their willingness to embrace social model approaches to disability. The chapter examines the repercussions of this reflection on the transformation of pre-service teacher programs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".