The Use of Social Media in Interprofessional Education: Systematic Review
Bibliographic record
Abstract
BACKGROUND: The implementation of interprofessional education (IPE) activities into health care education is a challenge for many training programs owing to time and location constraints of both faculty and learners. The integration of social media into these IPE activities may provide a solution to these problems. OBJECTIVE: This review of the published literature aims to identify health care IPE activities using social media. METHODS: The authors searched 5 databases (from the beginning coverage date to May 27, 2017) using keywords related to IPE and social media. Teams of 2 authors independently reviewed the search results to identify peer-reviewed, English language papers reporting on IPE activities using social media. They assessed the study quality of identified papers using the Medical Education Research Study Quality Instrument. RESULTS: A total of 8 studies met the review's inclusion criteria. Of these 8 papers, 3 had single-group, posttest-only study design; 4 had single-group, pre- and posttest design; and 1 had nonrandomized 3-group design. Qualitative and quantitative outcome measures showed mixed results with the majority of student feedback being positive. CONCLUSIONS: Despite a need for additional research, this review suggests that the use of social media may aid the implementation of health care IPE.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".