Increasing dissemination of Cochrane evidence using Social Media
Bibliographic record
Abstract
Background: Social media is currently defined as Internet-based tools that allow individuals and communities to share information and ideas (Ventola 2014). These tools include Twitter, Facebook, LinkedIn, YouTube and many more. As of the first quarter of 2017 Twitter had 328 million active users and Facebook had over 1.94 billion (Statista.com). Social media has been identified as a potential way to promote health behaviours and interact with healthcare practitioners and consumers (Ventola 2014). \n \nObjectives: To explore the use of social media to increase engagement between Cochrane New Zealand and healthcare practitioners, consumers and healthcare organisations \n \nMethods: This ongoing project began in September 2016. We set out to identify the key elements for posts that would encourage engagement in social media. The initial phase of the project concentrated on the use of Twitter and this has subsequently been followed by the second phase of the project concentrating on Facebook. Hootsuite, a scheduling and analysis software program, was utilised to plan social media posts and allow consumer engagement to be monitored. \n \nResults: We identified the key components of engaging posts as; a brief and intriguing tagline, the inclusion of related images, tags of the subject and local organisations with a relevant interest and finally an abbreviated link to the plain language summary. The number of Twitter followers has increased from by 267% (from 133 to 355). The total engagement (sum of interactions) is 466; 19 quotes, 225 retweets, 211 likes and 11 replies. New followers to Cochrane New Zealand on Twitter who are engaging with posts include Ageing Well New Zealand, Arthritis New Zealand, New Zealand Doctor, Otago Medical School and Plunket New Zealand which demonstrate the reach to local organisations. \n \nConclusions: Social media is a useful tool to increase dissemination of Cochrane evidence. This platform enables the timely transfer of the most recent findings to local organisations and consumers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".