Health Professions' Educators' Adaptation to Rapidly Changing Circumstances: The Ottawa 2020 Conference Experience
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Most health professions' educators (HPEs) are used to responding to change, whether these are longer term organisational changes or short term crises, e.g. staff or student sickness or technical systems' failures. Most of these changes, whilst they can be frustrating, typically have fairly straightforward and routine solutions. Other wider, environmental changes are also starting to affect educators, learners and the complex education and healthcare systems in which they operate, and these will have great impact in the relatively near future. However, it is the current crisis stemming from the global transmission of the coronavirus COVID-19 which has most recently impacted on HPE on a global scale. Whilst many of us are very used to working virtually and using social media and other activities to work collaboratively, we still tend to rely on regular meetings with friends and colleagues (old and new) around the world at conferences and meetings. Similarly, most universities rely primarily on face to face teaching to provide their programmes, particularly in the early years. The COVID-19 pandemic has put all that into sharp relief, and many of us are having to make quick and sometimes reactive adaptations to our best-laid plans. In this article, we discuss some of our experiences from the recent Ottawa 2020 conference held in Kuala Lumpur from 1-5 March 2020, identifying some of the lessons learned that educators around the world will need to keep in mind as we move into what is currently unchartered territory. The learning lessons from our experience are that safety is paramount, communication and transparency is key; flexibility is needed from all stakeholders; technologies can help, but be realistic; acknowledge the need for psychological adaptation to change and crisis and tap into the wisdom and collegiality of the community. This paper specifically refers to Covid-19 but the learning lessons are applicable to other major challenges and the ideas described transferable to other situations.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.053 | 0.013 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".