Clinical Ethics Consultation in Chronic Illness: Challenging Epistemic Injustice Through Epistemic Modesty
Bibliographic record
Abstract
Leading paradigms of clinical ethics consultation closely follow a biomedical model of care. In this paper, we present a theoretical reflection on the underlying biomedical model of disease, how it shaped clinical practices and patterns of ethical deliberation within these practices, and the repercussions it has on clinical ethics consultations for patients with chronic illness. We contend that this model, despite its important contribution to capturing the ethical issues of day-to-day clinical ethics deliberation, might not be sufficient for patients presenting with chronic illnesses and navigating as "lay experts" of their medical condition(s) through the health care system. Not fully considering the sources of personal knowledge and expertise may lead to epistemic injustice within an ethical deliberation logic narrowly relying on a biomedical model of disease. In caring "for" and collaboratively "with" this patient population, we answer the threat of epistemic injustice with epistemic modesty and humility. We will propose ideas about how clinical ethics could contribute to an expansion of the biomedical model of care, so that important aspects of chronic illness experience would flow into clinical-ethical decision-making.
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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 | 0.104 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.128 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".