Use of Peer-Led Web-Based Platforms for Peer-Assisted Learning Among Canadian Anesthesia Residents and Fellows: Cross-Sectional Study (Preprint)
Notice bibliographique
Résumé
BACKGROUND Peer-assisted learning (PAL) using peer-led web-based platforms (PWPs), including social media, can be a highly effective method of supporting medical trainees. PWPs, such as mobile apps for sharing anesthesia resources and social media groups or discussion forums pertaining to anesthesia training, may play a role in facilitating anesthesia trainee-led web-based education. However, there have been many challenges facing anesthesia trainees when it comes to incorporating PWPs, especially social media and mobile apps for PAL. OBJECTIVE The primary objective of this survey was to assess the proportion of trainees that use social media and mobile apps. The secondary objective was to identify the trainees’ perceptions on the use of social media and mobile apps for educational purposes, including PAL. METHODS This cross-sectional study was conducted through a survey administered via email at a single large academic center. The survey tool collected data between 2016 and 2017 on the following: demographic data (year of study, field of specialty), use of technology and web-based resources for medicine, use of social media platforms for anesthesia or training, benefits and barriers to future uses of social media for training, and ideas for trainee-led websites. Descriptive statistics were reported. RESULTS In total, 80 anesthesia trainees (51 residents and 29 fellows) responded to the survey (response rate of 33% of out 240 trainees contacted). All trainees reported having a mobile device that most (n=61, 76%) reported using multiple times a day to access medical resources. The highest perceived benefits of PWPs according to residents were that the most valuable information was available on-demand (n=27, 53%), they saved time (n=27, 53%), and they improved their overall learning experience within anesthesia (n=24, 47%). In comparison, fellows thought that PWPs were beneficial because they provided multiple perspectives of a single topic (n=13, 45%) and served as an additional platform to discuss ideas with peers (n=13, 45%). The most popular platforms used by both residents and fellows were Facebook (residents: n=44, 86%; fellows: n=26, 90%) followed by LinkedIn (residents: n=21, 42%; fellows: n=9, 29%). Even though most anesthesia trainees used social media for personal reasons, only 26% (n=21) reported having used resident- or fellow-driven PWP resources. Examples of PWPs that trainees used included anesthesia groups and a resident Dropbox resource folder. CONCLUSIONS There was generally an acceptance for using PWPs for PAL as they provided various benefits for trainees at all levels of learning. PWPs have the potential to garner an increased sense of community and sharing within learning experiences throughout all levels of training. The information gained from this survey will help inform the basis for developing an anesthesia trainee-led e-learning platform.
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 | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| 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 ».