Editorial: Advances in health education applying e-learning, simulations and distance technologies
Bibliographic record
Abstract
This special issue of the KM&EL international journal is dedicated to coverage of novel advances in health professional education applying e-Learning, simulations and distance education technologies. Modern healthcare is beginning to be transformed through the emergence of new information technologies and rapid advances in health informatics. Advances such as electronic health record systems (EHRs), clinical decision support systems and other advanced information systems such as public health surveillance systems are rapidly being deployed worldwide. The education of health professionals such as medical, nursing and allied health professionals will require an improved understanding of these technologies and how they will transform their healthcare practice. However, currently there is a lack of integration of knowledge and skills related to such technology in health professional education. In this issue of the journal we present articles that describe a set of novel approaches to integrating essential health information technology into the education of health professionals, as well as the use of advanced information technologies and e-Learning approaches for improving health professional education. The approaches range from use of simulations to development of novel Web-based platforms for allowing students to interact with the technologies and healthcare practices that are rapidly changing healthcare.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".