A Longitudinal Multi-Method Inquiry of Educational Workers’ Use of Interventions for Positive Mental Wellbeing
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Compassion fatigue and burnout are two distinct forms of mental health distress faced by educational workers. Researchers have shown a high level of both phenomena across the field of education; however, a better understanding of what educational workers already do for positive mental and emotional health is needed. METHODS: This research study examined three years of data, collected via survey, inquiring into the various interventions, namely supports and resources, that educational workers use to support positive mental health. RESULTS: Quantitative data analysis via descriptive and inferential statistics revealed that educational workers relied heavily on their personal support network followed distantly by medical professionals and other interventions, revealing a gap that needs to be addressed by employers. Qualitative thematic analysis revealed a trend towards increased use of environmental interventions to promote positive mental wellbeing. CONCLUSIONS: The data analysis suggested areas of focus required to ensure workplace wellbeing, and that programs too focused on individual or self-directed interventions would not be well received by educators. Suggestions for other interventions that might be helpful for leaders and policy-makers are provided.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".