Three Year Quantitative Study of Compassion Satisfaction and Fatigue Among Teachers and Educational Workers in Alberta, Canada
Bibliographic record
Abstract
Although the psychological workplace hazards of compassion satisfaction and compassion fatigue are a known risk factor for mental and emotional health distress for caring professionals, the extent of these hazards has not been explored in Alberta, Canada. Understanding and tracking the experiences of compassion fatigue and satisfaction of teachers and other educational workers was the primary focus of this three-year, cross-sectional research study. Methods: Multimethod, longitudinal study from June 2020-May 2023. Data were collected at three different time points between 2020 and 2023 to explore the mental and emotional health of teachers and other educational workers, and the quantitative analysis of this data suggests that mental and emotional health distress is widespread and intensifying across Alberta. Findings: This paper discusses the extent of compassion fatigue and satisfaction across Alberta both at a general level and related to years of experience in the education field. Data analysis suggests worsening workplace wellbeing over time in both number and intensity, across gender and job role. Discussion: This article provides further evidence of the deepening crisis in education and contains some suggestions for policymakers, teacher educators, and system decision-makers invested in improving workplace well-being in educational settings.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".