Interventions to Reduce Stress and Burnout among Teachers: A Scoping Review
Bibliographic record
Abstract
Background: Teaching is recognized as a highly challenging profession. Experience of chronic stress is a risk factor for poor mental and physical well-being, and burnout. There is limited knowledge regarding optimal interventions to address stress and burnout among teachers. Objective: To undertake a scoping review of the literature in the last five years to determine various psychological interventions to address stress and burnout among teachers. Method: The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews was followed. Relevant search terms were used to determine different interventions adopted to reduce teachers’ stress and burnout. Articles published between 2018 and 2022 were identified using five bibliographic databases. Relevant articles were extracted, reviewed, collated, and thematically analyzed, and findings s were summarized. Results: Forty studies conducted in Asia, North America, Oceania, Europe, and Africa, met the inclusion criteria. Sixteen kinds of burnout and stress-reduction interventions were identified. The most popularly studied intervention were Mindfulness-Based Interventions alone or in combination with yoga or Cognitive Behavioural Therapy (CBT), followed by Rational Emotive Behavioral Therapy (REBT). Mindfulness-Based Interventions led to decreased overall Teacher Stress Inventory (TSI) and emotional exhaustion subscale scores. REBT, primarily used with special education teachers, especially in Africa, has also shown positive results. Other interventions reporting positive outcomes include Inquiry-Based Stress Reduction (IBSR), the Stress Management and Resiliency Training Program (SMART), Cyclic Meditation, Group Sandplay, Progressive Muscle Relaxation, Autogenic Training, Sport-Based Physical Activity, Emotional Intelligence Ability Models and Christian Prayer and Prayer-Reflection. Conclusions: Stress and burnout can have a negative impact on teachers and, very often, on the students they teach. Implementing suitable school-based interventions is necessary to improve teachers’ stress-coping ability, reduce the likelihood of burnout and improve general well-being. Policymakers, governments, school boards and administrators should prioritize the implementation of school-based awareness and intervention programs.
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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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".