IMIA Accreditation of Biomedical and Health Informatics Education: Current State and Future Directions
Bibliographic record
Abstract
Summary Objectives: The educational activities initiated by the International Medical Informatics Association (IMIA) have had global impacts and influenced national societies and local academic programs in the field of Biomedical and Health Informatics (BMHI). After the successful publication and dissemination of its educational recommendations, IMIA launched an accreditation procedure for educational programs in BMHI. The accreditation procedure was pilot tested by several BMHI academic programs in different countries and continents to obtain a global perspective. Methods: This paper presents an overview of IMIA quality assurance and accreditation procedures along with feedback on issues and problems which emerged during the pilot. Results: It appears that IMIA quality assurance and procedures worked quite well in different countries of Europe, the Middle East, South America, and Asia. These first experiences provided adequate information for adapting, modifying, and optimizing the procedures and finally for the planning of future activities. Conclusions: IMIA accreditation framework comprises a single set of standards that apply at various levels to both academic and professional BMHI programs. The pilot phase confirmed the robustness and generalizability of quality assurance standards and associated procedures on which IMIA accreditation is based at an international level.
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 | 0.068 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".