蹂묒썝�쑄由ъ쐞�썝�쉶 �몴以��슫�쁺吏�移� 媛쒕컻: �빐�쇅 �궗濡�瑜� 以묒떖�쑝濡�
Bibliographic record
Abstract
Since the �쁁oramae Hospital�� case and �쁓everance Hospital�� case, most hospitals in South Korea have set up Hospital Ethics Committees(HECs). However, they haven�셳 worked well because of the absence of legislation and SOPs and a manpower shortage. Based on reviews of cases of SOPs of HECs in other countries such as the USA, Canada, and the UK, this paper will give the basic principles and contents of SOPs for HECs with a foundation of due process and independency.\n First, HECs must guarantee the best interests of the patients. Second, SOPs must ensure the flexibility to operate HECs according to their situations. Third, HECs must build up the ethical competences through the utilization of case consultation, policy development, and ethics education. Forth, HECs must have the professionalism to get the trust and reasoning power regarding their decisions. Fifth, HECs must be comprised of manpower that has various expertise and experiences. Sixth, HECs must operate through consistent procedures to get the due process. Seventh, HECs must try to ensure the principle of publicity and the participation of the patients to ensure transparency. Eighth, the chief of the institution has the responsibility for HECs to operate independently so that the members of HECs are able to act independently. Ninth, HECs have to maintain and improve the competences through continuous quality assessment. Tenth, all the documents of HECs have to be organized and conserved to ensure operating transparency and confidentiality. Eleventh, we propose the standard templates to promote operating effectiveness
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".