Social media in knowledge translation and education for physicians and trainees: a scoping review
Bibliographic record
Abstract
INTRODUCTION: The use of social media is rapidly changing how educational content is delivered and knowledge is translated for physicians and trainees. This scoping review aims to aggregate and report trends on how health professions educators harness the power of social media to engage physicians for the purposes of knowledge translation and education. METHODS: A scoping review was conducted by searching four databases (PubMed, Scopus, Embase, and ERIC) for publications emerging between 1990 to March 2018. Articles about social media usage for teaching physicians or their trainees for the purposes of knowledge translation or education were included. Relevant themes and trends were extracted and mapped for visualization and reporting, primarily using the Cook, Bordage, and Schmidt framework for types of educational studies (Description, Justification, and Clarification). RESULTS: There has been a steady increase in knowledge translation and education-related social media literature amongst physicians and their trainees since 1996. Prominent platforms include Twitter (n = 157), blogs (n = 104), Facebook (n = 103), and podcasts (n = 72). Dominant types of scholarship tended to be descriptive studies and innovation reports. Themes related to practice improvement, descriptions of the types of technology, and evidence-based practice were prominently featured. CONCLUSIONS: Social media is ubiquitously used for knowledge translation and education targeting physicians and physician trainees. Some best practices have emerged despite the transient nature of various social media platforms. Researchers and educators may engage with physicians and their trainees using these platforms to increase uptake of new knowledge and affect change in the clinical environment.
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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 | 0.040 | 0.155 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.036 | 0.038 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".