#GERIATRICS: AN ANALYSIS OF THE IMPACT OF THE GERIATRICS TWITTER NETWORK
Bibliographic record
Abstract
Abstract Twitter connects an international community of healthcare stakeholders, potentially augmenting access to information related to geriatric medicine. The purpose of this study is to analyze the geriatric medicine Twitter network, and we hypothesize this community has substantially grown in the last six years. We analyzed all publicly available tweets including the hashtag #geriatrics from January 1, 2013-January 1, 2019 using Symplur Signals, a health care social media analytics platform. We evaluated #geriatrics metrics over time related to activity, content analysis, user characteristics, engagement, and network analysis. A total of 159,008 tweets (containing 42.8% re-tweets) with the hashtag #geriatrics were written by 29,443 users, resulting in 393.6 million impressions. The number of tweets increased from 9,705 in 2013 to 39,151 in 2018 (32.2% compound annual growth); users increased from 3,366 in 2013 to 29,443 in 2018 (55.3% compound annual growth). Users were primarily found in the United States, United Kingdom, and Canada. The most commonly trending topic from 2013-2015 and from 2016-2018 was ‘older adults’. The top hashtags included in tweets with #geriatrics were #job, #aging, and #hpm (hospice and palliative medicine). Network analysis showed central hubs to be medical journals, provider organizations, individual physicians, and individual advocates. The top 150 influencers consisted primarily of physicians (49.1%), advocate/support organizations (13.8%), and media organizations (6.3%). The use of Twitter to promote geriatric medicine using #geriatrics has matured into an international digital community of interest. Future studies should examine hashtags related to age prevalent illnesses and hashtags likely to be used by patients.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".