Social media strategies used to translate knowledge and disseminate clinical neuroscience information to healthcare users: A systematic 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
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.
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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