Bibliographic record
Abstract
Ethics emerges from 'the theoretical domains of philosophy' and applied in modem medicine to assist healthcare staff in addressing an array of moral questions. However, ethical values between patient and healthcare staff often collided, leading to ethical conflicts and dilemmas in clinical settings. Ethics deliberation, a skill to resolve ethical conflicts, is not possessed by all. Controversies like the Seattle God Committee, Re Quinlan, Baby Jane Doe, and many others have contributed to the slow emergence of clinical ethics committees (CEC) to solve ethical dilemmas in the United States of America since the 1970s (Tapper, 2013). However, the number of CEC spurred when the Joint Commission on Accreditation of Healthcare Organisations (]CAHO) recommended that each hospital seeking its accreditation should have a mechanism to address ethical dilemmas within their institutions (Aulisio, 2016). Many changes have been made in the development of CEC around the world. CEC in some countries (the United States of America and Canada) are mandated by an accreditation body. Some countries (Belgium, Norway, and Singapore) had mandated the establishment of CEC by law while other countries (Ireland, the Netherlands, France, Germany, Italy, Spain, Switzerland, Denmark, Sweden, Lithuania, Croatia, Bulgarian, Israel, Japan, and New Zealand) reported the emergence of CEC based on grassroots phenomenon (Worthington & Macdonald, 2012). However, according to Khoo, Siew, Thong, Alwi, & Lantos (2019), there is no institution-based ethics consultation service available in Malaysia. There is a lacuna in the existing literature on the need and demand for Clinical Ethics Support Services (CESS) in Malaysia with the type of CESS that will be the most feasible for application in Malaysia. Using a library-based search method to elaborate on how other countries utilise CESS, the study will explore the various methods for delivering CESS, which will lead to the discussion on whether it is feasible to establish CEC in Malaysia. The study will justify why a modified model of a CEC is the most practical method to deliver CESS in Malaysia as an initiative to assist ethical decision-making in the healthcare industry that respects different values held by different parties for the benefit of both healthcare staff and patients.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".