Supporting Students with Mental Health Issues to Achieve Academic Success
Bibliographic record
Abstract
Positive mental health and well-being is imperative for students to succeed in school and to function successfully. The purpose of this study was to examine how elementary teachers support students with mental health problems in meeting academic success. In addition, the study aimed to examine teachers’ perceptions on their role in responding to students’ mental health needs; their feelings of preparedness in supporting these needs; and understanding their experiences with students suffering from mental health issues. Convenience sampling was used to obtain two Ontario Certified Teachers that worked with students with mental health issues. In this qualitative study, participant data was collected through semi-structured interviews and the data was organized into four themes: awareness of mental illness in students, strategies for supporting students with mental health problems, challenges in supporting students’ mental health and teachers supports and resources. Teachers reported to have some level of comfort in supporting students’ mental health needs, as well as having minimal formal mental health training in preservice education programs. The findings demonstrate the importance of being responsive to student’s needs, as well as creating positive classroom environments to alleviate mental health symptoms in students. Teachers also reported the need for more support within the classroom to meet the needs of their students.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".