Sharing Narratives to Foster Mental Health Literacy in Teacher Candidates
Bibliographic record
Abstract
This study explored the nature of teacher candidates’ mental health narratives in the context of completing an elective course in mental health and wellness. How students deconstructed their narratives and the narratives of their peers over time was also explored. Participants included 67 fourth-year students completing a five-year concurrent teacher education program. Data was collected over two academic years and consisted of students’ beginning-of-course and end-of-course narratives. The narratives were analyzed using content and thematic analysis. The findings are discussed in the context of using shared narratives as case study to promote self-reflection, discussion, problem-solving and mental health literacy within undergraduate courses. Cette étude explore la nature des récits sur la santé mentale racontés par des étudiants à l’enseignement en train de terminer un cours sur la santé mentale et le bien-être. Nous avons exploré la manière dont les étudiants ont décortiqué leurs récits ainsi que les récits de leurs pairs au fil du temps. Les participants consistaient de 67 étudiants de quatrième année inscrits dans un programme concomitant de formation des enseignants de cinq ans. Nous avons recueilli des données au cours de deux années universitaires; ces données représentaient des récits d’étudiants racontés au début du cours et à la fin du cours. Les récits ont été étudiés grâce à une analyse du contenu et des thèmes. Les résultats sont discutés dans le contexte du partage des récits en tant qu’études de cas afin de promouvoir l’auto-réflexion, la discussion, la résolution de problèmes et la compréhension de la santé mentale dans les cours de premier cycle.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".