The Quest for Identity of Health Informatics and for Guidance to Education in it – The German Reisensburg Conference of 1973 Revisited
Bibliographic record
Abstract
Abstract: Purpose: To review the minutes of the invitational workshop for defining the contents and approaches to education in and the professionalization of health informatics, which took place in Germany thirty years ago; To provide context for the meeting and assess its impact. Approach: The minutes resulting from the meeting were translated into English, and the literature attesting to the effects of the meeting was compiled in a literature review and commented on. Results: The meeting had profound effect in Germany, providing a model for several tiers of educational initiatives, and for professional recognition of the field of medical informatics. These were refined over the last thirty years and persist to this day. At the international level, the impact can be traced to the IMIA recommendations for education in health/medical informatics. More recent initiatives at defining the content of health informatics education did not result in fundamentally different models. Conclusion: One may assume then that the contents of education in medical/health informatics are well defined. The methods of education deserve greater attention.
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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.009 | 0.011 |
| 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".