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Record W7115022536 · doi:10.1093/pch/pxaf116.083

83 Canadian paediatric residents’ perspectives of social media in postgraduate medical education

2025· article· en· W7115022536 on OpenAlexaboutno aff

Bibliographic record

VenuePaediatrics & Child Health · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsnot available
Fundersnot available
KeywordsSubspecialtySocial mediaDescriptive statisticsCurriculumMedical school

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score0.748

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.027
GPT teacher head0.376
Teacher spread0.349 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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