Bibliographic record
Abstract
Understanding and accommodating patients ’ valuesis a key aspect of patient-centred care. Patients re-spond to illness and make health care decisions within a unique personal context shaped by culture, reli-gion, temperament and experience. The values and beliefs that they bring to health care decision-making may be the product of long and deep reflection, or may be largely un-examined. Many people faced with a crisis draw comfort and guidance from long-held beliefs; others find those be-liefs sorely tried. Some patients are distressed to manoeuvre through a health care system whose implied values do not closely mirror their own. Clinicians become involved in health care choices as facili-tators of the patient’s decision-making process. As such, they need an awareness of how their own cultural and religious background may influence their view of the patient’s situa-tion, as well as familiarity with religious and culturally based values different from their own. Although understanding and accommodating the unique cultural and religious views of patients — especially in relation to the ethical aspects of practice — is a critical determinant of quality of care, guid-ance for physicians on how to do so is not easy to locate in the medical literature. The first 17 articles in the Bioethics for Clinicians series appeared in CMAJ from July 1996 to October 1998. These articles, available on the series Web page (www.cma.ca/cmaj /series/bioethic.htm) and in book form,1 presented key con-cepts in contemporary bioethics, ranging from issues sur-rounding informed consent to dilemmas in end-of-life care, and provided practical guidance on applying these concepts in daily medical practice. With this issue we launch a new set of articles that extends the range of discussion into areas such as the disclosure of medical error and the determination of brain death. As with the first set, the purpose is to elucidate key concepts in bioethics that will help clinicians make well-reasoned and defensible decisions. In educational terms, the goal is to support performance: what clinicians actually do.2 These new articles include a subset that tackles head-on the complex issues that arise from the cultural diversity of the context in which Canadian physicians practise. Articles
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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.403 | 0.157 |
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".