Promoting student mental health through teacher and school psychologist collaboration: A participatory action research study
Bibliographic record
Abstract
Using participatory action research (PAR), five teachers and one school psychologist from one urban elementary school in Saskatchewan worked together on a mutual desire to promote positive student mental health. Together they established a collaborative group, narrowed the topic, developed research questions, and decided on data gathering through journals and a self-created rating scale. A document was to be created that would provide the teachers with strategies and language to address emergent student emotional needs. The study resulted in the creation of the Validation, Regulation and Collaboration Model (VRC Model) —a simple three step strategy that helps teachers with a range of experience support their students. The study found that by using the model teachers’ confidence in their ability to address typical daily student emotional needs in their classrooms had increased. An additional supporting model was also created, with both models having implications for teacher professional development for supporting student mental health in elementary schools. Results also indicated that the study itself provided positive professional development for the teacher participants and increased their understanding of working with school psychologists. Finally, the need for administrative support for both student and teacher mental health was also highlighted. Keywords: collaboration, educational psychologist, participatory action research, student mental health, teacher professional development
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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 | 0.035 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".