Using Rapid Design Thinking to Overcome COVID-19 Challenges in Medical Education
Bibliographic record
Abstract
The rapid rise of cases of coronavirus disease 2019 (COVID-19) has led to the implementation of public health measures on an unprecedented scale. These measures have significantly affected the training environment and the mental health of health care providers and learners. Design thinking offers creative and innovative solutions to emergent complex problems, including those related to training and patient care that have arisen as a result of the COVID-19 pandemic. Design thinking can accelerate the development and implementation of solution prototypes through a process of inspiration, ideation, and implementation. Digital technology can be leveraged as part of this process to provide care and education in new or enhanced ways. Online knowledge hubs, videoconference-based interactive sessions, virtual simulations, and technology-enhanced coaching for health care providers are potential solutions to address identified issues. Limitations of this model include inherent bias toward utilitarian instead of egalitarian principles and the subsequent threat to diversity, equity, and inclusion in solutions. Although medical educators have embraced digital transformation during the COVID-19 pandemic, there is a need to ensure that these changes are sustained.
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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".