Bibliographic record
Abstract
We thank the authors for their comments on the significance of learning experience design (LED) in adapting to digital interfaces in the delivery of health professions education. Co-creation and collaboration with learners in the design and testing of prototypes is crucial to the implementation of innovative digital solutions in medical education. 1 In addition to LED principles, learner competence in using virtual tools, appropriate mode of delivery (e.g., synchronous/asynchronous online learning), virtual simulation and virtual reality, as well as a continuous cycle seeking learner feedback and making improvements based on that feedback are crucial aspects of effective teaching through digital interfaces. Conceptually, LED is firmly rooted in a process of learning and continuous improvement. The COVID-19 pandemic has seen a rapid adoption of digital technology in medical education. LED’s focus on experiential learning and human centeredness are important considerations in educational innovation. 2 We believe these are crucial steps in the implementation phase of rapid design thinking. 1 The increased use of virtual tools and the blurred boundaries between work and home can lead to fatigue and burnout. 3,4 LED can help to guide wellness practices and strategies during the rapid upscaling of virtual tools. In a post COVID-19 era, LED must be applied beyond electronic user interfaces to sustain the benefits of design thinking. Emerging technologies such as artificial intelligence and learner analytics will play an important role in future adaptive learning. 5 Virtual tools have a significant role to play in medical education. The application of LED has the potential to increase the quality of learning and thus ensure high-quality medical education during and after the COVID-19 pandemic.
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 | 0.007 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.033 | 0.043 |
| Insufficient payload (model declined to judge) | 0.028 | 0.021 |
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".