Digital Interventions for Teacher Mental Health and Wellbeing: A Systematic Review and Meta-Analysis of Randomised Controlled Trials
Bibliographic record
Abstract
Mental health and wellbeing concerns are prevalent among teachers worldwide. Results from the 2023 International Barometer on Education Staff (I-BEST) surveying over 26,000 education personnel across 11 territories suggest that psychological health is a concern in many countries particularly Canada, France, and Belgium (Education and Solidarity Network, 2023). In a national survey of 4,245 Australian teachers, the Black Dog Institute found that almost half were considering leaving their job in the next 12 months and reported elevated levels of stress, anxiety, and depression compared to the general population and less than 20% of surveyed teachers reported feeling optimistic about the future (Black Dog Institute, 2023). Similarly, in a survey of 1,479 teachers in the United States, more than half of respondents reported experiencing frequent job-related stress and burnout and 22% reported challenges coping (Doan et al., 2024). High staff turnover and shortages, limited access to staff wellbeing support and resources, lack of time, and inadequate preparation to provide support after critical incidents are among the main contributors of poor mental health and wellbeing outcomes in teachers (Beyond Blue National Mental Health in Education Survey, 2024). Research also suggests that the mental health and wellbeing of teachers is closely tied to that of their students as greater teacher depressive symptoms are associated with poorer wellbeing and higher psychological distress in students (Harding et al., 2018). Interventions that support teachers’ mental health, wellbeing, and/or burnout are available but there are barriers to access (e.g., inconvenient locations and hours; Beames et al., 2023; Gearhart et al., 2022). In their meta-analysis and systematic review of interventions addressing teacher mental health, professional burnout, and/or wellbeing, Beames et al. (2023) found that most interventions use face-to-face modalities rather than online, requiring significant resources, time, and effort from participants. Mulvaney-Day et al. (2022) suggest that access to online behavioural health services post-pandemic may be an important consideration for employers when offering benefit packages to employees. Research further suggests that digital interventions show promise in the treatment of mental health conditions (Philippe et al., 2022). In their meta-analysis, Lehr et al. (2016) found that internet-based interventions can lead to reductions in depressive symptoms, perseverative thinking, and worry as well as improved sleep quality. The availability of technology-based healthcare is developing rapidly as the demand for more flexible healthcare increases (Lindardon et al., 2019). Therefore, an up-to-date systematic review and meta-analysis is needed to synthesise and assess the impact of digital interventions on teacher mental health and 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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.017 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".