The Impact of Teachers' Trust in Principal on Teacher Burnout
Bibliographic record
Abstract
This explanatory mixed methods study explored the relationship between teachers’ \nperceived trust in their principal and teacher burnout. This study also explores novice \nteachers’ lived experience of trust in their principal and stress. Snowball sampling \nthrough a public Facebook post was used to gather participants during the 2019-2020 \nschool year for an online survey. The survey was conducted using the Faculty Trust in \nPrincipal subscale of the Omnibus-T Scale and Maslach’s Burnout Inventory for \nEducators to survey 165 Ontario teachers. Follow-up semi-structured interviews were \nconducted with three novice teachers within the first 5 years of their careers to outline \ntheir lived experiences and identify traits of principals that indicate trustworthiness. \nResults identified a negative correlation between trust in principal and emotional \nexhaustion, depersonalization, as well as a positive correlation between trust in principal \nand sense of personal accomplishment. Results also indicated a connection between \nincreased faculty trust in principal when they had a shorter working relationship. The \nnovice teachers interviewed perceive that principals can develop their trust through the \nindividual consideration and idealized influence components of transformational \nleadership. Participants also identified principals reducing their workloads and trusting \nthem as important components for trust development. Novice Ontario teachers identified \nstress due to high expectations, precarious employment, and the pressure to build a \npositive reputation as influencers in trust 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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".