Ethics Certification of Health Information Professionals
Bibliographic record
Abstract
OBJECTIVES: To provide a model for ensuring the ethical acceptability of the provisions that characterize the interjurisdictional use of eHealth, telemedicine, and associated modalities of health care deliveiy that are currently in place. METHODS: Following the approach initiated in their Global Protection of Health Data project within the Security in Health Information Systems (SiHIS) working group of the International Medical Informatics Association (IMIA), the authors analyze and evaluate relevant privacy and security approaches that are intended to stem the erosion of patients' trustworthiness in the handling of their sensitive information by health care and informatics professionals in the international context. RESULTS: The authors found that while the majority of guidelines and ethical codes essentially focus on the role and functioning of the institutions that use EHRs and information technologies, little if any attention has been paid to the qualifications of the health informatics professionals (HIPs) who actualize and operate information systems to deal with or address relevant ethical issues. CONCLUSION: The apparent failure to address this matter indicates that the ethical qualification of HIPs remains an important security issue and that the Global Protection of Health Data project initiated by the SiHIS working group in 2015 should be expanded to develop into an internationally viable method of certification. An initial model to this effect is sketched and discussed.
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 | 0.049 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".