What are the focal points in bioethics literature? Examining the discussions about everyday ethics in Parkinson’s disease
Bibliographic record
Abstract
Everyday ethics refers to those issues which have a sometimes unrecognized moral dimension and that arise regularly within healthcare and research. These issues are often contrasted to dramatic ethics issues (i.e. issues that have seemingly higher stakes such as those arising in acute care situations or with invasive or life-threatening interventions). Claims have been made that scholarly bioethics tends to focus on dramatic ethics to the detriment of everyday ethics discussions. However, empirical evidence showing this has been lacking. Our own research investigating bioethics discussions in the Parkinson’s disease literature suggested this trend. Consequently, we decided to characterize the context and content of the Parkinson’s disease bioethics literature to empirically test the hypothesis that everyday ethics is under discussed. We conducted a broad literature search using the keywords “Parkinson’s disease” AND (“ethics” OR “bioethics”) and classified results inductively based on the context in which the bioethics discussion occurred. In line with our hypothesis and initial observations, we found that there is indeed a greater focus on dramatic ethics where topics such as deep brain stimulation and neuronal cell transplantations, dominated bioethics discussions. Given the potential utility of everyday ethics in improving healthcare and research, this mismatch in focus ought to be addressed. There is a clear need for further understanding and discussion of everyday ethical issues in scholarly bioethics.
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 | 0.067 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.013 | 0.041 |
| Scholarly communication | 0.029 | 0.041 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".