Mental Health Literacy in Educational Practice: A Qualitative Study Exploring New Elementary Teachers’ Reasoning when Identifying Students with Mental Health Concerns and Choice-Making in Supporting Referrals
Bibliographic record
Abstract
There are barriers to early identification of mental health conditions in children and access to timely interventions in school-based mental health services, leading to adverse long-term outcomes for students. Teachers are essential for early identification of mental health concerns and advocating for services, but a lack of mental health training can impede access to early intervention, leading to long-term deterioration in students’ mental health. This qualitative study used Think Aloud interviews with six early career elementary school teachers and two case-based scenarios to identify teachers’ cognitive processes to support their reasoning and choice-making when identifying students with mental health concerns and considering referring them to school-based mental health services. The interview transcripts and scenario responses were analyzed using reflexive thematic analysis to identify key themes and patterns in the teachers’ reasoning and choice-making processes. Results from this study showed that teachers use hypothesis-driven problem-solving, comparison-making, and the application of prior knowledge to understand students’ mental health concerns and make referrals to mental health services. Teachers also relied on information from their teacher training programs and professional experience to support their reasoning and choice-making, highlighting the importance of providing preservice teachers with mental health literacy training. The findings support other researchers’ recommendations that teachers’ mental health literacy, including knowledge about pathways to mental health services, be prioritized as part of teacher preparation programs. Furthermore, the results demonstrated the relevance of supplying preservice teachers with extensive and adaptable mental health literacy training as a strategy to augment their familiarity with student mental health concerns, as this may facilitate the early identification of students with mental health concerns and referrals to school-based mental health services.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".