Bibliographic record
Abstract
A recent study was done in Canada to identify what clinical ethicists felt were the top 10 clinical ethical challenges facing Canadians in healthcare. (Breslin et al ., 2005; Table VII.1). What is clear from this list is that many of these ethical issues are core to challenges of healthcare more broadly today. While this study was conducted in Canada, it is likely these same challenges may be similar in other healthcare systems, at least in the developed world. Clinical ethics is a comparatively recent endeavor in healthcare, but despite its relative newness it provides an ideal model for initiatives that can impact healthcare because of its inherent interdisciplinary make-up and its unique capacity to impact care across the healthcare spectrum from “boardroom to bedside.” While clinical ethics offers this unique perspective to address healthcare problems, it is often missing from the meetings where significant system-wide decisions are made. Many decision makers miss the key point that much of healthcare is grounded in values and many of the solutions may be found in the ethical field of inquiry. The most likely reason for this absence of clinical ethics at the decision tables in healthcare is related to the still developing nature of this work. What the chapters in this section show, however, is that perhaps clinical ethics is “coming of age” and is beginning to make serious arguments to the healthcare community about how its activities and frameworks can offer useful, real-world contributions to help to guide system decision makers, healthcare professionals, and the public.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.360 | 0.175 |
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".