School principals’ emotionally draining situations and student discipline issues in the context of work intensification
Bibliographic record
Abstract
Over the past decade, research into principals’ work intensification has revealed that principals spend significant work hours on student discipline and attendance issues, and that they report high levels of emotionally draining situations. In the current study, we examined the relationship between student discipline issues and principals’ emotionally draining situations to determine if variables related to student discipline issues affected principals’ experiences of emotionally draining situations. Using a correlational research design with hierarchical regression, we analysed data from a digital survey of school principals in Ontario, Canada. A total of 1434 surveys were included in the final analysis, with respondents from elementary, high-school and combined schools. Results showed a correlation between student discipline and attendance issues and principals’ experiences of emotionally draining situations, while also showing that student discipline and student and parent mental health were strong predictors of principals’ experiences of emotionally draining situations. These findings have important implications in supporting principals: These insights can inform principal preparation programmes by showing the need for increased training on identifying and treating emotionally draining situations. Insights may also encourage policymakers to review student discipline and student/parent mental health policies in light of the revelation of their impact on principals’ work.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 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".