Bibliographic record
Abstract
BACKGROUND AND AIM: use among paramedics and other prehospital care clinicians is on the rise and is increasingly being used as a platform for continuing education and international collaboration. In 2014, the hashtag #FOAMems was registered. It is used for the sharing of emergency medical services, paramedicine, and prehospital care-related content. It is a component of the 'free open-access meducation' (FOAM) movement. The aim of this study was to characterize and evaluate the content of #FOAMems tweets since registration. MATERIALS AND METHODS: An analytical report for #FOAMems was generated on symplur.com from February 4, 2014, to April 30, 2017. A transcript of all #FOAMems tweets for a randomly selected 1 month period (October 2015) was generated, and quantitative content analysis was performed by two reviewers. Tweets were categorized according to source (original tweet/retweet) and whether referenced. The top 92 tweeters were analyzed for professional identity. RESULTS: During the study period, there were over 99,000 tweets containing #FOAMems, by over 9,200 participants. These resulted in almost 144 million impressions. Of the top 92 tweeters, 50 were paramedics (54%). Tweets were mainly related to cardiac (23%), leadership (19%), and trauma (14%). The 1-month period resulted in 649 original tweets, with 2110 retweets; 1070 of these were referenced. CONCLUSION: using #FOAMems. Social media resources are widely shared, which is in line with the FOAM movement's philosophy. However, opportunities exist for paramedics to share further diverse resources supported by referenced material.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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; 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".