Bibliographic record
Abstract
Having just returned from an International Medical Informatics Association (IMIA) working group meeting on health and medical informatics education, I would like to reflect on what I learned and to share this with readers.The theme of the conference was Building Worldwide Capacity for the Health Informatics Workforce.The IMIA education working group seeks to advance knowledge of how health and medical informatics is taught: • to health care professionals around the world; • to students of computer science/informatics; • within dedicated curricula in health and medical informatics.Since 1974, the IMIA working group has sponsored nine conferences aimed at disseminating and exchanging information on Health and Medical Informatics programmes and courses. 1 The October 2008 conference, in Buenos Aires, Argentina, was truly international, with representatives from 17 countries and six continents.Although there were no librarians in attendance, many of the papers and debates would be of interest to health science librarians.The workshops and keynote papers covered six topics: 1 Case Studies of Programmes and Projects.2 Curriculum; History of Health Informatics Education.3 Occupational Standards, Competencies, Benchmarking; Career Opportunities. 4 Educational Methods/Resources/Approaches. 5 International Initiatives.6 Information Retrieval.All the papers and PowerPoints from this event are online at http://www.
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 | 0.034 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.022 | 0.030 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".