A Pathway for Student Voice in System Mental Health Planning: A case study exploring the use of YPAR to inform school mental health initiatives
Bibliographic record
Abstract
Student mental health and well-being are foundational for learning. Ideally, student mental health action plans and system initiatives are created collaboratively between system leaders and those most directly impacted, the students. While the importance of including student voice is highlighted in both policy and practice throughout the education sector, the pathway to meaningfully engage student voice is less clear. Youth Participatory Action Research (YPAR) provides a framework for youth to partner and collaborate with other researchers and stakeholders to study a problem and develop actions that are aligned with their needs and priorities. Through a single case study design, this research project aimed to explore the use of YPAR as a pathway to engage student voice in school system mental health initiatives. This study explored the use of YPAR with secondary students and myself, the Mental Health Lead, as co-researchers at a provincial Catholic school board during the 2021-2022 school year. At the conclusion of the YPAR project, the student researchers and staff on the Mental Health Advisory Committee involved in this YPAR initiative participated in separate focus group discussions in order to reflect and share their experiences participating in this project. Reflexive thematic analysis was used to identify key themes to describe the experiences of students and staff participating in the YPAR process. My own self-reflections as the school board Mental Health Lead and YPAR co-researcher were also captured related to implications for practice as well as future research in areas of school mental health and student voice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.015 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".