State of play of CME in Europe in 2012: proceedings from the fifth annual meeting of the European CME Forum
Bibliographic record
Abstract
One hundred and thirty speakers and delegates met in London for the fifth annual meeting of the European CME Forum which took place on 15 and 16 November 2012. Over the two days, current and future trends in European Continuing Medical Education (CME) were examined, discussed and debated. The meeting employed a mixture of styles: plenary presentations, workshops and panel discussion, but with a high focus on open question-and-answer interaction between speakers and delegates.The predominant target audience comprised people with an interest in European CME including the accreditation bodies, scientific societies, education providers, medical communications agencies and industry supporters.Each interest group had a dedicated meeting on the day before the formal programme; European pharmaceutical company involvement, scientific societies and the Good CME Practice Group which announced that following the publication of its recent work, it was opening its membership to all qualifying providers. A pre-meeting needs assessment was carried out which informed the programme structure and led to a more interactive format.Session themes included the new European Accreditation Council for CME (EACCME) accreditation criteria, e-learning, panel discussions with experts from throughout Europe as well as CME professionals from the USA, Canada, India and Australia.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".