Teacher mental health literacy and its effects on helping behaviors for students with mental health problems
Bibliographic record
Abstract
Abstract Aim School teachers are expected to support students with mental health problems. However, few studies have examined actual helping behaviors of teachers for the students. This study aimed to investigate the behaviors of Japanese teachers for students experiencing internalizing problems (e.g., depression/anxiety). Methods In 2020, teachers (n = 465) from 48 Japanese schools (primary/junior high/senior high) answered a self‐administered questionnaire asking about: (a) the number of students in their homeroom class who seemed to have internalizing problems during the last 2–3 months, (b) whether they asked these students how they had been feeling lately, and (c) the number of students who answered “not feeling well.” Mental health literacy (MHL) in teachers was also assessed. Results Most of the teachers (80.2%) reported that they dealt with one or more students who seemed to have internalizing problems during the last 2–3 months. Among these teachers, 94.7% had asked at least some of the students how they had been feeling, and over half of the teachers (57.8%) reported that at least one of the students answered “not feeling well.” Teachers who had confidence in helping students with depressive symptoms were more likely to recognize students who were “not feeling well” (p = 0.04). Conclusion Teachers appear to be willing to help students with mental health problems when they recognize symptoms of the problems. Future studies will benefit from more closely examining whether improving confidence in teachers through MHL training increases students' willingness to disclose mental health problems to teachers, an important step in the prevention/treatment of these problems.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".