Do Healthcare Institutions Have Consciences and a Resultant Ethical Right to Institutional Conscientious Objection?
Bibliographic record
Abstract
In healthcare, Institutional Conscientious Objection (ICO) occurs when a healthcare institution (e.g., a hospital) refuses to provide a healthcare service (e.g., abortion) on the grounds that this service contradicts its conscience. Debate rages over whether governments should allow ICO in healthcare. Currently, some do, and some don’t. Among other reasons, the debate matters because ICO can have dramatic effects on patients’ ability to access healthcare services. The debate encompasses many topics, but this paper focuses on the arguments regarding institutional conscience. This topic matters because it is legally important in many countries, and because it is central to the ICO debate; both pro-ICO arguments and anti-ICO arguments are often built on claims about the existence or nonexistence of institutional conscience, respectively. This paper’s overarching question is as follows: do healthcare institutions have consciences and a resultant ethical right to ICO? I answer this question by separating it into two sub-questions. The first is as follows: do healthcare institutions have consciences? I argue no. What I add to the literature here is in-depth critical analysis of the debates over what is necessary to satisfy the criteria for conscience, and I bring in lessons from the philosophy of group agency. Unlike the vast majority of ICO opponents, I charitably engage with the best and most recent arguments for institutional conscience (in particular, work by Xavier Symons and Reginald Mary Chua). The second question is as follows: if healthcare institutions do have consciences, does it follow that they thereby have an ethical right to ICO? Again, I argue no. I answer this question via a highly novel and highly systematic approach, which involves precisely conceptually dissecting healthcare institutions, and I bring in lessons from the philosophy of group rights.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.016 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".