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.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".