#WhyWeDoResearch: Raising research awareness and opportunities for patients, public and staff through Twitter
Bibliographic record
Abstract
The #WhyWeDoResearch campaign was set up in 2014 and was originally planned to run locally, in Norfolk, at the James Paget University Hospitals NHS Foundation Trust (JPUH) for 12 days in December. Within four days, the campaign was being utilized nationally by other trusts and charities. By the New Year of 2015 it became international and had reached Australia and Canada. The intended audience for the campaign is broad and includes: patients, the general public, all staff working in health care and/or research including (but not limited to) National Health Service (NHS), commercial companies, charities and schools. The campaign has become a community where patients, staff and public alike can share their voices about health research on an equal playing field. Each year, to coincide with International Clinical Trials Day (ICTD) on 20 May, a #WhyWeDoResearch 'Tweetfest' is hosted. This includes a number of 'tweetchats' at set times throughout the Tweetfest. Tweetchats are hosted by experts in particular diseases or other areas. Patients and patient groups are included in this group of experts. This article uses the #WhyWeDoResearch campaign annual Tweetfest to demonstrate how social media can be utilized to raise awareness of health research around the world.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.065 | 0.027 |
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".