A Repository of Codes of Ethics and Technical Standards in Health Informatics
Bibliographic record
Abstract
We present a searchable repository of codes of ethics and standards in health informatics. It is built using state-of-the-art search algorithms and technologies. The repository will be potentially beneficial for public health practitioners, researchers, and software developers in finding and comparing ethics topics of interest. Public health clinics, clinicians, and researchers can use the repository platform as a one-stop reference for various ethics codes and standards. In addition, the repository interface is built for easy navigation, fast search, and side-by-side comparative reading of documents. Our selection criteria for codes and standards are two-fold; firstly, to maintain intellectual property rights, we index only codes and standards freely available on the internet. Secondly, major international, regional, and national health informatics bodies across the globe are surveyed with the aim of understanding the landscape in this domain. We also look at prevalent technical standards in health informatics from major bodies such as the International Standards Organization (ISO) and the U. S. Food and Drug Administration (FDA). Our repository contains codes of ethics from the International Medical Informatics Association (IMIA), the iHealth Coalition (iHC), the American Health Information Management Association (AHIMA), the Australasian College of Health Informatics (ACHI), the British Computer Society (BCS), and the UK Council for Health Informatics Professions (UKCHIP), with room for adding more in the future. Our major contribution is enhancing the findability of codes and standards related to health informatics ethics by compilation and unified access through the health informatics ethics repository.
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 | 0.030 | 0.197 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.050 | 0.054 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.015 | 0.028 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.032 |
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".