Use of Peer-Led Web-Based Platforms for Peer-Assisted Learning Among Canadian Anesthesia Residents and Fellows: Cross-Sectional Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".