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Enregistrement W4284975247 · doi:10.2196/39404

Development and Implementation of Ontario Critical Care Clinical Practice Rounds

2022· article· en· W4284975247 sur OpenAlexaffvenueabout
Zoya Adeel, Neill K. J. Adhikari, Josée Theriault, Bernard Lawless, Maria Cheung, Lynn Ward, Michael Sullivan, David Neilipovitz

Notice bibliographique

RevueIproceedings · 2022
Typearticle
Langueen
DomaineMedicine
ThématiqueCOVID-19 and healthcare impacts
Établissements canadiensOttawa HospitalSt. Michael's HospitalToronto General HospitalCARE CanadaHealth Sciences NorthUniversity of TorontoSunnybrook HospitalHealth Sciences CentreSunnybrook Health Science Centre
Organismes subventionnairesnon disponible
Mots-clésHealth carePandemicPublic relationsKnowledge translationPresentation (obstetrics)Best practiceNursingCoronavirus disease 2019 (COVID-19)MedicineMedical educationPolitical sciencePsychologyKnowledge managementComputer science

Résumé

récupéré en direct d'OpenAlex

Background The COVID-19 pandemic brought unprecedented challenges to health care systems across the world. Health care professionals were burdened with time constraints as they balanced care for a large number of patients while managing crippling resource shortages. In the pandemic’s early stages, it was challenging for health care providers to provide evidence-based therapies due to the novel nature of COVID-19. There were also pressures to adopt unproven, yet highly touted treatments based on media reports and social media postings. These challenges were identified by Critical Care Services Ontario (CCSO), a provincial health organization that ensures the integration of the critical care system in Ontario, Canada. Since traditional methods of knowledge translation were inaccessible during the pandemic, CCSO created a webinar series titled Ontario Critical Care Clinical Practice Rounds (OC3PR) to share evidence-based practices with critical care professionals. Objective We sought to develop and implement a webinar series to connect critical care professionals with the best available evidence and clinical expertise during the COVID-19 pandemic. We were also interested in gathering attendee perceptions of OC3PR as an educational tool. Methods CCSO collaborated with 5 regional critical care leaders in Ontario to develop and implement OC3PR. This committee identified presentation topics based on perceived urgency and demand and selected presenters with expertise on their respective discussion topic. To promote accessibility, OC3PR was facilitated on the Zoom platform, live simulcasted on Youtube, and subsequently posted on Youtube for asynchronous viewing. Attendees also had the opportunity to share inquiries in live questions-and-answers sessions facilitated by the presenters. Finally, to gather the perceptions of and experiences with OC3PR, we invited attendees to partake in a web-based questionnaire at the end of each session. Results In total, 19 webinars were presented from November 26, 2020, to December 2, 2021, with 1481 registered unique attendees from within Canada and internationally and 17,533 Youtube visits. OC3PR presentation topics centered on resource rationing, patient therapies, staffing challenges, infection control, and vaccination. In addition, 22 follow-up questionnaires yielded 408 responses from attendees, which were composed of physicians (32%), registered nurses (15%), and other health care professionals. Our survey results suggest that OC3PR is beneficial to professionals as the majority of the respondents strongly agreed that it was of acceptable quality, enhanced their knowledge, relevant to their practice, and allotted appropriate timing for interactive components. Most (98%) respondents also reported that they would attend another OC3PR session. Conclusions The success of OC3PR as an accessible educational tool made it evident to CCSO that it would continue this forum in a postpandemic context. Although the webinar series was created for critical care professionals, it can be adapted by other health organizations to improve the integration of their health networks and enhance the support they provide for their workers. Conflicts of Interest None declared.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,540
Score d'incertitude au seuil0,997

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,126
Tête enseignante GPT0,510
Écart entre enseignants0,385 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission3
Résumé présentoui

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