The Effectiveness of Social Media in the Dissemination of Knowledge About Pain in Dementia
Bibliographic record
Abstract
OBJECTIVES: Traditional knowledge dissemination methods have been ineffective in leading to timely and widespread changes in clinical practice. Social media have the potential to reach broader audiences than more traditional methods that disseminate research findings. We evaluated the effectiveness of using social media to mobilize knowledge about pain in dementia. METHODS: We developed an online repository of evidence-based content (e.g., guidelines, assessment approaches) and a video about pain in dementia. The video was uploaded to YouTube, a video-sharing platform. We collaborated with stakeholder organizations on a 5-month social media campaign (#SeePainMoreClearly) on Twitter, a social networking site, to disseminate digital content about pain in dementia. The response to our initiatives was evaluated with Web and social media metrics, a video questionnaire, and a comparison of the extent of Twitter discussions about pain in dementia before and during the campaign period. RESULTS: Web metrics showed a great reach of the initiative: The #SeePainMoreClearly hashtag received more than 5,000,000 impressions and was used in 31 countries. The online repository was viewed by 1,218 individuals from 55 countries, and the video resulted in 51,000 views. Comparisons between the pre-campaign and campaign periods demonstrated a higher number of posts about pain in dementia during the campaign period. CONCLUSION: The findings have implications for closing the knowledge-to-practice gap in dementia care through faster mobilization of scientific findings. Our campaign compares favorably with other health information dissemination initiatives. The methodologies used in the study could serve as a framework for the development of social media initiatives in other health disciplines.
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How this classification was reachedexpand
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 | 0.059 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".