The Effectiveness of Web-Based Psychotherapy to Treat and Prevent Burnout: Controlled Trial
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».