What do we know about interventions to improve educator wellbeing? A systematic literature review
Bibliographic record
Abstract
Abstract This systematic literature review summarises the research into interventions intended to improve the wellbeing of educators in the early childhood to secondary sectors. A search of articles published between 2000 and 2020 yielded 23 articles that met our inclusion criteria. Studies were included if they collected quantitative or qualitative data about educator wellbeing pre-intervention and post-intervention from the same group(s) of educators. We classified articles into five categories based on their content: multi-foci (several content areas included in a program), mindfulness, gratitude, professional development (classroom practice oriented), and physical environment. The articles revealed wide variations in: wellbeing theories underpinning interventions, the phenomena measured, and the effectiveness of the interventions. In some studies wellbeing was conceptualised as the absence of negative states (such as stress), in other studies to the presence of positive states (such as satisfaction), and in a few studies as the combination of both these approaches. Some of the gaps noted across the research include the lack of attention to the role of the school climate in determining the success of an intervention, and the lack of analysis to explore whether interventions work better for some individuals than others (for example, a lack of reporting of the characteristics of participants who drop out of the interventions). Overall, the multi-foci interventions show the most promise for improving educator 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 | 0.039 | 0.186 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.010 |
| Bibliometrics | 0.024 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".