A new day for CME/CPD in Canada: proceedings from the 1st Canada Regional Conference of the Global Alliance for Medical Education in Montreal, Canada
Bibliographic record
Abstract
The Global Alliance for Medical Education (GAME) is a not-for-profit organization founded in 1995, with the aim of advancing innovation in medical education. The 1st GAME Canada regional conference was held in Montreal on May 22, 2015, under the leadership of Suzanne Murray, who acted as programme chair, and GAME president Lisa Sullivan. The conference brought together a broad array of speakers and panellists, including experts from academic centres, health systems, accreditors, private organizations, and industry. Thirty-one key stakeholders participated in the event, demonstrating a strong commitment towards the improvement of best practice in continuing medical education (CME)/continuing professional development (CPD). The conference included diverse presentations providing opportunities for reflection and discussion throughout the day. The participants actively took part in stimulating discussions that covered a large range of topics, including the need for enhanced networking and opportunities to learn from others, the challenges of assessment and the potential solutions, interprofessional education and competencies, and, finally, the future of a Canadian CME/CPD organization.
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 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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.014 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.024 | 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".