Bibliographic record
Abstract
This action research project examined school leaders’ role in supporting classroom teachers’ mental health within Prince Edward Island schools. The study explored the question: How can Prince Edward Island’s school leaders support the mental health needs of teachers in Prince Edward Island (PEI)? Using action research methodology, and specifically an action research engagement model I interviewed three classroom teachers from across PEI with between three and thirteen years of experience and conducted a focus group with five school principals from various schools in PEI with experience ranging from four to eighteen years. I undertook this work with the approval of the Royal Roads University Ethics Board, the Department of Education Early Learning and Culture’s (DEEC) External Research Review Committee and in line with the Royal Roads University Ethics Policy. A literature review investigated leadership models and teacher mental health and competencies fundamental to supporting teacher mental health. Key themes include teacher workload, teacher appreciation, teacher autonomy, teacher mental health, teacher collaboration, and the leadership competencies included in the ethical, appreciative, and servant leadership models. The key findings of this study explored the link between school leadership and teachers’ mental health and the ability of school leaders to create school cultures conducive to supporting and improving teachers’ mental health. The major recommendations offered actions the DEEC could undertake to facilitate leadership development in school leaders using leadership models supportive of teachers’ mental health and create opportunities to increase collaboration and shared learning that could foster schools as supportive, collaborative learning networks tasked with improving the collective capacity of students.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".