Social Media Use by Health Care Professionals and Trainees
Bibliographic record
Abstract
PURPOSE: To conduct a scoping review of the literature on social media use by health care professionals and trainees. METHOD: The authors searched MEDLINE, CENTRAL, ERIC, PubMed, CINAHL Plus Full Text, Academic Search Complete, Alt Health Watch, Health Source, Communication and Mass Media Complete, Web of Knowledge, and ProQuest for studies published between 2000 and 2012. They included those reporting primary research on social media use by health care professionals or trainees. Two reviewers screened studies for eligibility; one reviewer extracted data and a second verified a 10% sample. They analyzed data descriptively to determine which social media tools were used, by whom, for what purposes, and how they were evaluated. RESULTS: The authors included 96 studies in their review. Discussion forums were the most commonly studied tools (43/96; 44.8%). Researchers more often studied social media in educational than practice settings. Of common specialties, administration, critical appraisal, and research appeared most often (11/96; 11.5%), followed by public health (9/96; 9.4%). The objective of most tools was to facilitate communication (59/96; 61.5%) or improve knowledge (41/96; 42.7%). Thirteen studies evaluated effectiveness (13.5%), and 41 (42.7%) used a cross-sectional design. CONCLUSIONS: These findings provide a map of the current literature on social media use in health care, identify gaps in that literature, and provide direction for future research. Social media use is widespread, particularly in education settings. The versatility of these tools suggests their suitability for use in a wide range of professional activities. Studies of their effectiveness could inform future practice.
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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 | 0.033 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".