The Effectiveness of Internet-Based Cognitive Behavioral Therapy as a Preventive Intervention in the Workplace to Improve Work Engagement and Psychological Outcomes: Protocol for a Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Mental health has become an increasingly significant issue in the workplace. Non-health care workers are experiencing increased levels of psychological symptoms in their workplaces, especially during the COVID-19 pandemic, which limited social interactions and health service access. These conditions have a negative effect on employees' mental health and may also be associated with work-related outcomes, such as reduced levels of work engagement. Cognitive behavioral therapy (CBT) is one of the most effective methods used for treating workers with mental illness and preventing work-related psychological outcomes. The delivery of internet-based CBT (iCBT) has been established as a result of both technological improvements that have influenced health promotion and prevention components, and limited social contact and health service access. OBJECTIVE: The purpose of this systematic review is to synthesize the best available evidence concerning the preventive effect of iCBT on employees. METHODS: A systematic search will be conducted across 12 electronic databases, including a hand search for main journals and reference lists. Randomized controlled trials testing the effects of iCBT on psychological outcomes and work engagement among employees will be eligible. Initial keywords will cover the concepts of employees, workers, non-health care personnel, internet-based, web-based, eHealth cognitive behavioral interventions, stress, depression, anxiety, and work engagement, and then a full search strategy will be developed. Following titles, abstracts and the full text will be screened for assessment against the inclusion criteria for the review. Search results will be fully reported and presented per Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Two independent reviewers will screen and extract data, appraise methodological quality using the Cochrane risk-of-bias assessment tool, and assess overall quality of evidence with the Grading of Recommendations Assessment, Development, and Evaluation approach. A random effects meta-analysis and standardized mean differences using review manager software will be applied to synthesize the effect of iCBT based on similar outcomes. RESULTS: This protocol was registered in the International Prospective Register of Systematic Reviews in March 2022 and is now an ongoing process. The data will be analyzed in August 2022, and the review process should be completed by December 2022. All included studies will be synthesized and presented to demonstrate the effectiveness of iCBT in decreasing psychological distress and optimizing work engagement outcomes among employees. CONCLUSIONS: According to the findings of this study, iCBT therapies will be used to promote mental health concerns such as depressive symptoms, anxiety, psychological distress, stress, insomnia, and resilience among non-health care professionals. In addition, the results will be used to ensure the policy related to reducing psychological distress and optimizing work engagement in the workplace. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/38597.
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 | 0.059 | 0.071 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.028 | 0.036 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.059 | 0.006 |
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".