Digital Self-Guided Mental Health Interventions to Prevent Workplace Burnout and Enhance Psychological Wellness: Protocol for a Systematic Review
Bibliographic record
Abstract
Background: Employee burnout has reached critical levels, with nearly 40% of workers reporting symptoms driven by excessive workloads, inadequate managerial support, and toxic organizational cultures. Research has consistently indicated that workplace stress significantly impacts employees' physical health. Consequently, a variety of self‑guided mental health programs have been developed to address these challenges. Despite numerous evidence‑based self‑guided mental health interventions, there remains a lack of understanding and clarity regarding which specific content modules, intervention features, active components, and theoretical frameworks most effectively drive meaningful improvements in workplace mental health. Objective: This systematic review aims to synthesize evidence on self-guided digital mental health interventions in workplace settings. Specifically, it seeks to identify which content, design features, activities, and assignments are most effective for preventing burnout and enhancing psychological well-being. This review will also examine underlying theoretical mechanisms and assess the methodological rigor of included studies to provide actionable recommendations for intervention developers and organizational stakeholders. Methods: A systematic search will be executed across PsycINFO, PubMed, Web of Science, and Cochrane Library for relevant studies published since 2000, using comprehensive search strings targeting working adults, digital delivery modes, outcomes related to burnout prevention, stress reduction, well-being, and controlled experimental designs. Titles, abstracts, and keywords will be screened, with additional records identified through manual searches of reference lists. Following the removal of duplicates, a 2-step screening process will be applied to studies based on the defined inclusion and exclusion criteria. Data from eligible studies will be extracted into a standardized Excel template covering authors, sample characteristics, study design, intervention content and theoretical framework, outcome measures, intervention effectiveness, implementation fidelity, and risk-of-bias assessment. Results: Preliminary searches were conducted in early 2025. The review is anticipated to be completed by May 2026. Conclusions: This review will identify theoretical mechanisms and core components driving the effectiveness of self-guided digital interventions for workplace burnout, stress, and psychological well-being. By identifying theoretical background, rationale, content, activities, characteristics, and implementation factors behind evidence-based digital interventions, the findings will guide the development of scalable and accessible programs that enhance employee wellness, boost productivity, and inform future organizational mental health and workplace wellness initiatives.
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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 | 0.062 | 0.066 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.018 | 0.017 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.080 | 0.012 |
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".