Stress, Burnout, Anxiety and Depression among Teachers: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Worldwide, stress and burnout continue to be a problem among teachers, leading to anxiety and depression. Burnout may adversely affect teachers' health and is a risk factor for poor physical and mental well-being. Determining the prevalence and correlates of stress, burnout, anxiety, and depression among teachers is essential for addressing this public health concern. OBJECTIVE: To determine the extent of the current literature on the prevalence and correlates of stress, burnout, anxiety, and depression among teachers. METHOD: This scoping review was performed using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Relevant search terms were used to determine the prevalence and correlates of teachers' stress, burnout, anxiety, and depression. Articles were identified using MEDLINE (Medical Literature Analysis and Retrieval System Online), EMBASE (Excerpta Medica Data Base), APA PsycINFO, CINAHL Plus (Cumulative Index of Nursing and Allied Health Literature), Scopus Elsevier and ERIC (Education Resources Information Center). The articles were extracted, reviewed, collated, and thematically analyzed, and the results were summarized and reported. RESULTS: When only clinically meaningful (moderate to severe) psychological conditions among teachers were considered, the prevalence of burnout ranged from 25.12% to 74%, stress ranged from 8.3% to 87.1%, anxiety ranged from 38% to 41.2% and depression ranged from 4% to 77%. The correlates of stress, burnout, anxiety, and depression identified in this review include socio-demographic factors such as sex, age, marital status, and school (organizational) and work-related factors including the years of teaching, class size, job satisfaction, and the subject taught. CONCLUSION: Teaching is challenging and yet one of the most rewarding professions, but several factors correlate with stress, burnout, anxiety, and depression among teachers. Highlighting these factors is the first step in recognizing the magnitude of the issues encountered by those in the teaching profession. Implementation of a school-based awareness and intervention program is crucial to resolve the early signs of teacher stress and burnout to avoid future deterioration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.018 | 0.015 |
| 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) | 0.003 | 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".