Social media as an open-learning resource in medical education: current perspectives
Bibliographic record
Abstract
PURPOSE: Numerous studies evaluate the use of social media as an open-learning resource in education, but there is a little published knowledge of empirical evidence that such open-learning resources produce educative outcomes, particularly with regard to student performance. This study undertook a systematic review of the published literature in medical education to determine the state of the evidence as to empirical studies that conduct an evaluation or research regarding social media and open-learning resources. METHODS: The authors searched MEDLINE, ERIC, Embase, PubMed, Scopus, and Google Scholar from 2012 to 2017. This search included using keywords related to social media, medical education, research, and evaluation, while restricting the search to peer reviewed, English language articles only. To meet inclusion criteria, manuscripts had to employ evaluative methods and undertake empirical research. RESULTS: Empirical work designed to evaluate the impact of social media as an open-learning resource in medical education is limited as only 13 studies met inclusion criteria. The majority of these studies used undergraduate medical education as the backdrop to investigate open-learning resources, such as Facebook, Twitter, and YouTube. YouTube appears to have little educational value due to the unsupervised nature of content added on a daily basis. Overall, extant reviews have demonstrated that we know a considerable amount about social media use, although to date, its impacts remain unclear. CONCLUSION: There is a paucity of outcome-based, empirical studies assessing the impact of social media in medical education. The few empirical studies identified tend to focus on evaluating the affective outcomes of social media and medical education as opposed to understanding any linkages between social media and performance outcomes. Given the potential for social media use in medical education, more empirical evaluative studies are required to determine educational value.
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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 | 0.023 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".