Twitter as a Mechanism of Knowledge Translation in Health Professions Education: An Exploratory Content Analysis
Bibliographic record
Abstract
Introduction: Social media may facilitate knowledge sharing within health professions education (HPE), but whether and how it is used as a mechanism of knowledge translation (KT) is not understood. This exploratory study aimed to ascertain what content has been shared on Twitter using #MedEd and how it is used as a mechanism of KT. Methods: Symplur was used to identify all tweets tagged with #MedEd between March 2021 - March 2022. A directed content analysis and multiple cycles of coding were employed. 18,000 tweets were identified, of which 478 were included. Studies sharing high quality HPE information; relating to undergraduate, postgraduate, or continuing education; referring to an evidence source; and posted in English or French were included. Results: Diverse content was shared using #MedEd, including original tweets, links to peer-reviewed articles, and visual media. Tweets shared information about new educational approaches; system, clinical, or educational research outcomes; and measurement tools. #MedEd appears to be a mechanism of diffusion (n = 296 tweets) and dissemination (n = 164 tweets). It is less frequently used for knowledge exchange (n = 13 tweets) and knowledge synthesis (n = 5 tweets). No tweets demonstrated the ethically sound application of knowledge. Discussion: It is challenging to determine whether and how #MedEd is used to promote the uptake of knowledge into HPE or if it is even possible for Twitter to serve these purposes. Further studies exploring how health professions educators use the knowledge gained from Twitter to inform their educational or clinical practices are recommended.
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 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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".