The Effectiveness of Web-Based Psychotherapy to Treat and Prevent Burnout: Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Burnout is a hidden productivity killer in organizations. Finding a solution to efficiently measure and proactively prevent or rehabilitate employees with burnout is a challenge. To meet this unabated demand, companies and caregivers can focus on proactive measures to prevent "Burnout as an Occupational Phenomenon." OBJECTIVE: We aimed to address effectiveness, reliability, and validity of the empowerment for participation (EFP) batch of assessments to measure burnout risk in relation to the efficacy of web-based interventions using cognitive behavioral therapy (CBT) and floating to improve mental health and well-being. We introduced three risk assessments: risk for burnout, risk of anxiety, and risk for depression. METHODS: We used an interventional, empirical, and parallel design using raw EFP psychometric data to measure the effectiveness of web-based therapy to reduce the risk of burnout between a control group and web-based therapy group. A total of 50 participants were selected. The rehabilitation and control groups consisted of 25 normally distributed employees each. The rehabilitation group received therapy, whereas the control group had not yet received any form of therapy. IBM SPSS was used to analyze the data collected, and a repeated measures ANOVA, an analysis of covariance, a discriminant analysis, and a construct validity analysis were used to test for reliability and validity. The group was selected from a list of employees within the My-E-Health ecosystem who showed a moderate or high risk for burnout. All assessments and mixed-method CBT were web-based, and floating was conducted at designated locations. The complete EFP assessment was integrated into a digital ecosystem designed for this purpose and therapy, offering a secure and encrypted ecosystem. RESULTS: There was a statistically significant difference between pre- and postassessment scores for burnout. The reliability of the burnout measure was good (Cronbach α=.858; mean 1.826, SD 3.008; Cohen d=0.607; P<.001) with a high validity of 0.9420. A paired samples 2-tailed test showed a good t score of 4.292 and P<.001, with a good effect size, Cohen d=0.607. Web-based therapy reduced the risk for burnout in participants compared with the control group. Tests of between-subject effects show F=16.964, a significant difference between the control group and the web-based therapy group: P<.001, with movement between the group variables of 0.261 or 26.1% for the dependent variable. CONCLUSIONS: This study suggests good reliability and validity of using web-based interventional mixed methods CBT to reduce the risk of burnout. The EFP batch of web-based assessments could reliably identify morbidity risk levels and successfully measure clinical interventions and rehabilitation with consistently reliable results to serve as both a diagnostic and therapeutic tool worthy of major research in the future. TRIAL REGISTRATION: ClinicalTrials.gov NCT05343208; https://clinicaltrials.gov/ct2/show/NCT05343208.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".