Turning around a medical education conference: Ottawa 2020 in the time of COVID‐19
Bibliographic record
Abstract
The Ottawa 2020 Conference on the Assessment of Health Professionals and Evaluation of Programmes was held from 29 February to 4 March 2020 in Kuala Lumpur, Malaysia, co-organised by the Association for Medical Education in Europe and the International Medical University (http://ottawa2020.org/). Between late January and early February 2020, reports of the spread of the coronavirus disease 2019 (COVID-19) cases led to concerns about its impact on Ottawa 2020. By mid-February, 10% of participants, many of whom were presenters, had withdrawn from the conference due to either personal reasons, work or travel restrictions. The potential impact of further participant withdrawals on the programme and on scholarly engagement was of major concern to the organisers, as was the ability to provide a safe and welcoming environment for participants. Following a risk assessment, a decision was taken not to postpone or cancel Ottawa 2020. All possible steps were taken to safeguard the well-being of attendees, based on the World Health Organization guidelines February 20201 and advisory from the Ministry of Health Malaysia. A needs assessment informed the transformation to a ‘blended conference,’ with a mix of face to face and online content to support those attending on site and those participating at a distance. Limited resources, in particular the 15-day timeline to transform the event, were major considerations. Additional roles and responsibilities were identified, and a streaming service provider was engaged to live stream all plenaries and symposia to registered participants. Key speakers who were unable to attend were able to either present live or to send a recorded presentation and join a live question and answer session with both on site and online participants, the latter monitored by an on-site moderator. Participants with oral presentations were able to send videos, and poster presenters could submit Portable Document Formats (PDFs) with quick response (QR) codes for subsequent contact. Technologies appropriate for the context were selected, detailed instructions for moderators, speakers and participants were communicated, and a plan for a post-conference repository was formulated. Almost 700 participants attended the conference in person. For each of the streamed sessions, online participation ranged from 50 to 95. Although both on site and online participants generally evaluated the conference well, a number of online participants requested for more online moderators to improve engagement. The streaming and repository were appreciated by many.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.005 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.072 | 0.017 |
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".