Digital Mindfulness Training for Burnout Reduction in Physicians: Clinician-Driven Approach
Notice bibliographique
Résumé
BACKGROUND: Physician burnout is widespread in health care systems, with harmful consequences on physicians, patients, and health care organizations. Mindfulness training (MT) has proven effective in reducing burnout; however, its time-consuming requirements often pose challenges for physicians who are already struggling with their busy schedules. OBJECTIVE: This study aimed to design a short and pragmatic digital MT program with input from clinicians specifically to address burnout and to test its efficacy in physicians. METHODS: Two separate nonrandomized pilot studies were conducted. In the first study, 27 physicians received the digital MT in a podcast format, while in the second study, 29 physicians and nurse practitioners accessed the same training through a free app-based platform. The main outcome measure was cynicism, one dimension of burnout. The secondary outcome measures were emotional exhaustion (the second dimension of burnout), anxiety, depression, intolerance of uncertainty, empathy (personal distress, perspective taking, and empathic concern subscales), self-compassion, and mindfulness (nonreactivity and nonjudgment subscales). In the second study, worry, sleep disturbances, and difficulties in emotion regulation were also measured. Changes in outcomes were assessed using self-report questionnaires administered before and after the treatment and 1 month later as follow-up. RESULTS: Both studies showed that MT decreased cynicism (posttreatment: 33% reduction; P≤.04; r≥0.41 and follow-up: 33% reduction; P≤.04; r≥0.45), while improvements in emotional exhaustion were observed solely in the first study (25% reduction, P=.02, r=.50 at posttreatment; 25% reduction, P=.008, r=.62 at follow-up). There were also significant reductions in anxiety (P≤.01, r≥0.49 at posttreatment; P≤.01, r≥0.54 at follow-up), intolerance of uncertainty (P≤.03, r≥.57 at posttreatment; P<.001, r≥0.66 at follow-up), and personal distress (P=.03, r=0.43 at posttreatment; P=.03, r=0.46 at follow-up), while increases in self-compassion (P≤.02, r≥0.50 at posttreatment; P≤.006, r≥0.59 at follow-up) and mindfulness (nonreactivity: P≤.001, r≥0.69 at posttreatment; P≤.004, r≥0.58 at follow-up; nonjudgment: P≤.009, r≥0.50 at posttreatment; P≤.03, r≥0.60 at follow-up). In addition, the second study reported significant decreases in worry (P=.04, r=0.40 at posttreatment; P=.006, r=0.58 at follow-up), sleep disturbances (P=.04, r=0.42 at posttreatment; P=.01, r=0.53 at follow-up), and difficulties in emotion regulation (P=.005, r=0.54 at posttreatment; P<.001, r=0.70 at follow-up). However, no changes were observed over time for depression or perspective taking and empathic concern. Finally, both studies revealed significant positive correlations between burnout and anxiety (cynicism: r≥0.38; P≤.04; emotional exhaustion: r≥0.58; P≤.001). CONCLUSIONS: To our knowledge, this research is the first where clinicians were involved in designing an intervention targeting burnout. These findings suggest that this digital MT serves as a viable and effective tool for alleviating burnout and anxiety among physicians. TRIAL REGISTRATION: ClinicalTrials.gov NCT06145425; https://clinicaltrials.gov/study/NCT06145425.
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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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».