130 Adaptation of IHI Joy-in-Work framework to reduce burnout among postgraduate trainees
Notice bibliographique
Résumé
Abstract Primary Subject area Physician Wellness Background Physician burnout is a psychological phenomenon with serious and pervasive consequences on physicians’ mental health, patient safety, and quality of care. Burnout is multifactorial, originating from systemic issues, organizational culture and individual coping skills. Burnout is more common in residency training. Residents experience burnout more intensely due to lack of autonomy, self-efficacy and exposure to mistreatment. Residents are also frontline workers and the future healthcare givers. Organization-led interventions mostly focus on building resilience and mindfulness without addressing systems-level issues. In our study, we attempted to shift the paradigm to address system-level concerns first. We aimed to adapt Joy-in-Work: a quality improvement framework developed by the Institute for Healthcare Improvement (IHI). This program allows residents to identify system problems that are meaningful to them and empower them to work as a team, taking back their autonomy and self-efficacy. Objectives To demonstrate that Joy-in-Work can be adapted effectively into a residency training program to reduce burnout and improve psychological safety among residents. Design/Methods The four steps of Joy-in-Work were implemented for residents in a level 3 neonatal intensive care unit. Residents engaged in “what matters” conversations through survey and group meetings, and identified impediments to Joy-in-Work. By applying QI methodology, residents identified priority interventions to eliminate impediments. Finally, the effectiveness of interventions was evaluated. Primary outcomes included prevalence of burnout and psychological safety; secondary outcomes assessed control over workload, and organizational culture. An IHI 12-item questionnaire was administered at baseline and after the interventions. To assess sustainability, a survey was also conducted one year after the implementation. We assessed adherence to interventions, nurse practitioners’ satisfaction and residents’ workload indicators. Results Through the implementation of Joy-in-Work, residents identified autonomy and work life integration as priorities. Stakeholders developed two interventions: change call schedule according to residents’ preferences and earlier afternoon handover time. Burnout was 77.8%, 50% and 75% for three survey periods respectively. Psychological safety increased consistently from 16.7% to 37.5% to 43.8%. Lack of control over workload dropped sharply from 72.2% to 12.5%, with a rebound to 56.3%. Most secondary outcomes demonstrated a similar pattern of positive change initially with reversion to baseline. Conclusion We demonstrated that Joy-in-Work is successfully adaptable into a residency setting. Implementation through residents’ engagement and empowerment can decrease burnout and improve psychological safety significantly. The process itself was likely the key driver for achieving positive outcomes rather than the actual interventions. Sustainability remains a key issue that requires systems support.
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 | 0,005 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».