From manifestos to praxis: developing criticality in healthcare simulation
Bibliographic record
Abstract
The authors of ‘a manifesto for healthcare simulation practice ’ have crafted a call to action for the healthcare simulation community in response to the COVID-19 pandemic.1 They rightly point to the unprecedented disruption and chaos that this crisis has wrought in our personal lives, our societies and most acutely in healthcare. With great tempo, health professional’s have had to develop new ways of providing care for our populations. Similarly, health professions educators have had to be responsive and adaptive in preparing our healthcare workforce and students—with little of the development lead-in time on which they have come to rely. Within the manifesto, simulationists are being called to incorporate ‘a more comprehensive understanding of healthcare simulation beyond tool, technique or experience and instead recognise it as a professional practice’ with attendant responsibilities and accountabilities.1 These responsibilities include ‘adopting a commitment to comprehensive safety, to advocate collaboratively and lead ethically .’ 1 These noble words reflect the passion and urgency felt by the authors in the face of the disruption and destruction rendered by the pandemic within our field of healthcare simulation. As evidenced by the prolific numbers of COVID-19-related articles, research projects and presentations online - the simulation community across the globe is rising to this new and unique challenge. Manifestos look to the future. More than promise, they make public their pledge to drive change for new lines of action in our global simulation community. While looking forward, we argue that we must also look inward into our simulation community and critically ask ourselves questions about our community, values and relationships within the wider arena of health professions education. By drawing on concepts and ideas, theories help us to understand the world around us. Critical theories are one such group of theories that support us in challenging …
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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 | 0.092 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.105 |
| Scholarly communication | 0.034 | 0.049 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.011 | 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".