Social media in knowledge translation and education for physicians and trainees: a scoping review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it