Depression and anxiety among K–12 teachers in the United States: A systematic review.
Bibliographic record
Abstract
Teachers experience high levels of stress and burnout; however, it is less clear whether teachers also experience high levels of depression and anxiety. The purpose of this systematic review was to describe the literature examining depression and anxiety among K-12 teachers in the United States, with a focus on (a) identifying factors that may be associated with and (b) describing interventions aimed at improving depression and/or anxiety among teachers. A literature search was conducted in January 2022 using APA PsycInfo, ERIC, CINAHL, Web of Science, and PubMed. Studies were eligible if they (a) measured U.S. K-12 teachers as an outcome; (b) measured teacher depression or anxiety; (c) were available in English; and (d) were published between 2000 and 2021. Two coders extracted key study information and assessed the risk of bias using the Newcastle-Ottawa scale for observational studies and the U.S. Preventive Services Task Force checklist for clinical trials. This review included 19 studies (10 cross-sectional, four longitudinal, and five interventions). Studies indicated that teachers may experience greater levels of depression and anxiety than the general population. High perceived stress, poor coping skills, more student problem behaviors, and poor school climate were associated with greater depression and anxiety among teachers. Interventions achieved small to large reductions in depression and anxiety. This review suggests that several factors are related to depression and anxiety among teachers and is limited by the few studies that met the inclusion criteria. Interventions that use multilevel approaches to improve teacher mental health may be needed. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".