Social media strategies used to translate knowledge and disseminate clinical neuroscience information to healthcare users: A systematic review
Bibliographic record
Abstract
Social media can be an important source of clinical neuroscience information for healthcare users (e.g., patients, healthcare providers, the general public). This systematic review synthesized evidence on the effectiveness of social media strategies in translating knowledge and disseminating clinical neuroscience information to healthcare users. A systematic review of six electronic databases up to July 29, 2024 was conducted. Original, peer-reviewed articles examining the effectiveness of YouTube, Facebook, LinkedIn, Twitter, social media messaging apps, or a combination of these platforms in translating clinical neuroscience information to healthcare users (e.g., patients, healthcare providers, caregivers, and the general public) were eligible for inclusion. Several proxies (e.g., change in uptake of research, change in awareness, change in knowledge, change in understanding, behaviour change, and/or change in social media metrics) were considered as outcomes of knowledge translation (KT) effectiveness. Two independent reviewers screened articles and assessed risk of bias. The protocol was registered on PROSPERO (ID: CRD42021269034). A total of six studies were included in this review. The included studies used YouTube, Facebook, Twitter, or a combination of social media platforms aimed at healthcare users. Most social media strategies used to disseminate clinical neuroscience information in the included studies (N = 5/6) resulted in improved indicators of KT. However, due to the high risk of bias among the included studies, these results must be interpreted with caution. Disseminating clinical neuroscience information via Facebook, Twitter, YouTube, or a combination of these platforms may achieve the goals of KT. However, there is currently a gap in the literature about clinical neuroscience KT via social media, both in the quantity of studies and quality of evidence. Future research should aim to minimize the risk of bias by controlling for important confounding factors and use objective measures of KT to complement subjective measures.
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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 | 0.027 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".