86 Sleep and Fatigue in Residency: Identifying Needs for a Pilot Fatigue Risk Management Plan
Notice bibliographique
Résumé
Abstract Background Fatigue is inevitable in healthcare professions, given that patient care is provided 24 hours a day, 7 days a week. Due to the potential for fatigue to decrease both physiological and cognitive performance, fatigue in healthcare professionals comes with an elevated risk for both patients and providers. Residents in particular are at high risk for fatigue given the demands of post-graduate training. Fatigue negatively impacts a resident physician's own health and wellbeing and, importantly, increases the risk of serious medical errors affecting patient safety. Given that fatigue is an inevitable aspect of healthcare training, it is a resident training program's responsibility to ensure that residents can work safely when fatigued to minimize harm to themselves and their patients. The National Steering Committee on Resident Duty Hours in Canada recommended that all residency programs develop a Fatigue Risk Management Plan (FRMP) to address this issue. Objectives The University of Ottawa Pediatrics Residency program was tasked to create and pilot a fatigue risk management plan (FRMP) for their residents with support from the Royal College of Physicians and Surgeons of Canada (RCPSC); the first step in the project involved determining the present state of fatigue in the training program. The aim of this study was to perform a baseline fatigue scan of current residents to further quantify the risk and explore the residents’ experience involving fatigue during their training. Design/Methods Twenty-six pediatric residents completed demographic surveys and 14-day diaries to assess sleep quantities/patterns and pre- and post-shift fatigue levels. Focus groups further explored fatigue experiences during training. Quantitative data identified shifts with high-risk of fatigue-related errors, with previous research suggesting cut-offs of <5h of sleep in the previous 24h or <12h in 48h, and shift length > 10h. Qualitative thematic analysis was performed to draw key themes from the focus groups. Results Sleep diaries captured 111 work shifts, with longest shifts being 26h on-call. Our data showed that 48.6% of shifts were greater than 10h, 11.9% of shifts residents had <12h of sleep in the preceding 48h, 2% of shifts residents had <5h in the past 24h. 12.9% of pre-shift wakefulness levels and 30.9% of post-shift levels were in the severe (score 8-9) range. Analysis of focus groups identified key themes: (1) Organizational Fatigue Prevention/Management Strategies, (2) Organizational culture factors influencing perceptions/attitudes towards fatigue, (3) Recognizing Fatigue in Self, and (4) Barriers to Sleep/Contributing Factors to Fatigue at Work. Conclusion This study provides a baseline of current residents’ fatigue levels, suggesting a proportion of shifts were high-risk for fatigue-related errors based on prior sleep-wake data. Key themes identified potential topics for educational interventions as well as targets for organizational improvements with regards to fatigue risk management. This data will be the basis of next steps to implement an FRMP within our organization.
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 | 0,010 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».