Exploring clinical decision-making in pediatric concussion and the impact of the COVID-19 pandemic
Notice bibliographique
Résumé
Concussions are a significant public health concern in Canada. The effective development and implementation of a pan-Canadian concussion strategy requires an understanding of the current state of concussion policies across the country. Given the recent COVID-19 pandemic, there is also an interest in the impact on clinicians, as well as facilitators and barriers to the remote management of concussion. This project focuses on Ontario, British Columbia, and Alberta. These three provinces have taken different approaches to concussion management. Ontario is the only province in Canada with concussion specific legislation. British Columbia’s approach to concussion policy involves the development of clinical guidelines to assist in awareness and management of concussion. Alberta’s approach involves the development of a provincial concussion strategy to support families, children, and clinicians managing concussion. At the onset of the COVID-19 pandemic, Ontario had a provincial telehealth network, while Alberta initially did not allow for virtual care. British Columbia had a hybrid model, with limited telehealth services. These initial differences impacted how clinicians’ managed concussion during the COVID-19 pandemic. The purpose of this project is to explore factors that guide how healthcare professionals manage pediatric concussion, understand perspectives of recent clinical practice guidelines and resulting changes in practice, and to explore the impact of the COVID-19 pandemic on healthcare practices, including the provision of remote telehealth/telerehab services. Eighteen interviews were conducted with physiotherapists, occupational therapists, and physicians from Ontario, Alberta, and British Columbia. Interviews were thematically analyzed and organized to identify common challenges. This study identified multiple key findings. Clinicians are playing a more active role in managing patients’ concussions, however there is a lack of harmonization in concussion management both across Canada and provincially. Although clinicians were unaware if a pan-Canadian concussion strategy current exists, the 5th Consensus Statement on Concussion in Sport, the Ontario Neurotrauma Foundation Living Guidelines, and Parachute resources, were identified as the most used resources, respectively. Concussion awareness has increased over recent years due to the Internet, media, and movies, but in some instances, this awareness is excessive and may result in hyperawareness of concussion. Waitlists and outdated or incorrect advice have been identified as challenges to effective and timely concussion care. Barriers to remote care include a lack of existing remote care infrastructure and poor technology and internet connection. Facilitators to remote care include changes to billing practices and better virtual platforms. Participants with pre-existing remote care experienced less disruption during the COVID-19 pandemic.
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 | 0,025 | 0,049 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,010 | 0,010 |
| Communication savante | 0,010 | 0,004 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».