Is it Too Optimistic to Assume Light Touch Interventions can Improve Educational Workers’ Wellbeing? Insights from a Field Randomized Control Trial in Canada
Bibliographic record
Abstract
Educator wellbeing has broad implications for students and schools. Current approaches to address this problem are generally resource-intensive. This trial used novel nudges to increase wellbeing and decrease burnout among educators and other school-based faculty. We designed a light touch intervention where T1 received evidence-based wellbeing weekly text messages and T2 received weekly messages plus leadership endorsement emails. We evaluated this intervention in a large-scale three-arm RCT with participants (n=1,155) from K-12 schools in Manitoba, Alberta, and British Columbia. When compared to the control group, we saw no significant difference between the control group and T1 and T2 groups on burnout or wellbeing. The failure of these evidence-based text messages in increasing educators’ wellbeing and reducing their burnout highlights both the difficulty of addressing this problem and the importance of learning lessons from trials with null results to contribute to our knowledge base of improving educators’ wellbeing.
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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 | 0.033 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".