The Cascade Effect of an Eight-Week smartEducation Program for Educators: An Inductive Qualitative Content Analysis
Bibliographic record
Abstract
Interest in integrating mindfulness-based interventions (MBIs) into primary and secondary education systems to reduce educator burnout and attrition is rapidly increasing at a policy level. However, most MBIs focus on delivering mindfulness to students over educator wellbeing, leaving little resources for wellness-focused training. Additionally, MBI research to date has focused on effectiveness through quantitative approaches, resulting in the omission of subjective perspectives. In the current study, we examined a complementary program–Stress Management and Resiliency Techniques Education (smartEducation)–which focuses on developing mindfulness skillsets in educators. Through a retrospective inductive qualitative content analysis of 86 participants’ reflections, we sought to clarify educators’ perspectives on the effects of the program and the potential mechanisms of change through which the program had these effects. We found that (a) smartEducation participants experienced positive program effects and acquisition of mindfulness skills that affected individual wellbeing as well as producing a significant “cascade effect” downstream (e.g., students, colleagues) and (b) found support for four potential mechanisms of change. The smartEducation program represents a preventative mental health initiative tailored to educators. With the current levels of burnout and attrition amongst educators, further research, policies, and implementation of complementary approaches are more vital than ever to bolster educational communities.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".