Abstract 18760: Evaluation of a Web-based Cardiac Pain Knowledge Dissemination Platform Fueled by Social Media
Bibliographic record
Abstract
Introduction: The prevalence of persistent cardiac pain-related conditions is on the rise. Increasingly prevalent forms of persistent cardiac pain include refractory angina, cardiac syndrome X and functional cardiac pain syndromes. Our aim was to disseminate a web-based, multi-media resource centre, CardiacPain.Net, as a prototype for large-scale knowledge dissemination and educational outreach in partnership with Elsevier. Methods: Multi-media design features included open access published resources, narrated video, roundtable discussions, downloadable fact sheets, and an asynchronous discussion forum. Accreditation for continuing medical education (Canada and United States) was secured to maximize incentive for end-user uptake. Dissemination strategies included opt-in email blasts to Elsevier subscribers (n= 27,000) every 3 months, e-banner advertising, and mass social media via Twitter. Standard dissemination metrics included total site visits and components visited and downloaded. Customized metrics included unique and return visitor rates, bounce rate, stream views, and geo-targeting. All metrics were analyzed in aggregate and examined for outliers and monthly trends. Interrupted time series analysis with an autoregressive structure was used to examine immediate impact of social media on 12-month dissemination metrics. Results: CardiacPain.Net reached end users in 132 countries across 5 continents. A total of 7,066 unique visitors were engaged and resources were viewed and downloaded 13,629 times. The monthly return visitor and bounce rates were 30% and 50%, respectively. Average time spent on multi-media presentations was 14.5 minutes. Twitter constituted 66% of all total social media engagement; 62% of Twitter users were female. Interrupted time series analyses revealed that Twitter outreach significantly increased end user sessions, number of users, and page views (p< 0.001). Twitter did not affect proportion of new users monthly or website navigation behavior. Conclusions: Results suggest that as a large-scale knowledge dissemination and online cardiac educational outreach prototype, CardiacPain.Net is far reaching and capable of sustained engagement of an international-scale audience.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".