Secondary school students' perceptions and experiences of school mental health climate
Bibliographic record
Abstract
As 10-20% of Canadian youth will experience a mental illness at some point, many efforts have been made in recent years to prioritize adolescent mental health across Canada, including British Columbia. Given the unique advantages schools have in reaching a large number of adolescents, the school has been identified as an essential setting in which to address and support students’ mental health. The school climate is an important component of a student’s school experience and much is known about the relationship between school climate and student mental health. On the other hand, the climate particularly towards mental health in Lower Mainland secondary schools has largely been unexplored. As a school climate towards mental health may have significant implications on students’ mental health, including their help-seeking behaviour, it is important to determine how schools are addressing and supporting students’ mental health from the student perspective. Using Interpretative Phenomenological Analysis (IPA) as the methodological framework, the purpose of the present study was to explore and understand students’ perceptions and experiences of the current climate towards mental health in Lower Mainland secondary schools. Seven Grade 11 and 12 students from secondary schools across the Lower Mainland of British Columbia participated in semi-structured interviews about their perceptions and experiences surrounding how mental health is addressed and supported at school. Following the step-by-step data analysis procedure outlined within IPA, five broad and 14 subthemes were identified. While participants generally reported being supported at school when it comes to academic support and the availability of individuals to talk to about mental health challenges, they also acknowledged certain limitations about mental health support at school. Conversations surrounding mental health reportedly rarely occur; however, participants discussed the language used by students when it is talked about and the importance of relationships when disclosing mental health problems. Furthermore, students’ perceived mental health literacy and the stigma associated with mental health problems varies across schools. Findings are discussed within the context of the existing literature. Finally, future directions for research and for school-based practices as reported by participants, and implications for the practice of school psychologists are also discussed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".