ETHICAL ISSUES SURROUNDING THE USE OF INFORMATION IN HEALTH CARE
Bibliographic record
Abstract
As a result of rapid technological and economic expansion throughout the world, society is confronted with new requirements. For the success of the medical practice, even with the rapid changes in technology and the medical field, practitioners involved in the use of patients’ information are obliged to continue to behave ethically. This paper reviews the ethical challenges raised in the use of patients’ information for medical and other purposes. It also discusses the values underlining the ethical issues and their importance in the use of patients’ information in the doctor and patient context. The issues surrounding the use of patients’ information such as secrecy and confidentiality are raised and potential problems in the area, policy issues which must be addressed by those concerned with the confidentiality and secrecy of health information and the germane legal issues are also discussed. Moreover, this is a review of the current status of the health care information ethics with particular reference to the United Kingdom, United States, Canada, Australia and developing countries. Finally, it concludes that the emerging field of health care information ethics will require careful thought and insights from an international collection of ethicists.
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 | 0.195 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.068 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.025 | 0.026 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".