83 Canadian paediatric residents’ perspectives of social media in postgraduate medical education
Bibliographic record
Abstract
Abstract Background Social media is emerging as a potential tool within medical education. Despite its increased use in academia generally, there are limited studies that assess social media’s use in postgraduate medical education. Objectives This study’s objective is to assess the perspectives of Canadian paediatric residents towards the use of social media in their training and to highlight specific future uses of social media in medical education. Design/Methods Current paediatric residents and paediatric subspecialty residents (PGY1-6) training in Canada who were fluent in written English were eligible to participate. An electronic questionnaire created for this study was administered using REDCapTM and explored respondents’ social media usage as well as the perceived benefits and barriers of social media in medical education. The survey was distributed via paediatric program directors and remained open for approximately 6 weeks. Responses were analyzed using descriptive statistics. Results The survey was distributed to an estimated 196 paediatrics residents and subspecialty trainees and received 45 responses (response rate 23%). All respondents (45/45, 100%) reported engaging with social media. “De-stressing” was the most common reason indicated (43/45, 96%). Nearly half (49%, 22/45) reported using social media for both personal and professional purposes. While almost all participants (44/45, 98%) encountered medical education content on social media, only 36% (16/45) actively used social media for this purpose. The most commonly used platforms were social networking sites (44/45, 98%) and media-sharing sites (34/45, 76%). About 27% (12/45) of respondents reported applying knowledge learned from social media to patient care. Approximately half (25/45, 56%) of respondents reported that social media should be used more frequently in paediatric resident medical education. The most commonly reported barriers to using social media for medical education included concerns about the quality / accuracy of educational information (42/45, 93%), lack of established guidelines from regulatory bodies (35/45, 78%), and blurring one’s personal and professional life (35/45, 78%). Conclusion Paediatric residents use social media for a variety of purposes. Although a minority of respondents actively seek out medical education content, more than half felt social media could be used more in medical education. Programs may be able to support residents interested in learning through social media by compiling a list of reputable, high-quality resources. Future research may help assess the feasibility and impact of integrating social media into medical education.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".