NAVIGATING THE MURKY INTERSECTION BETWEEN CLINICAL AND ORGANIZATIONAL ETHICS: A HYBRID CASE TAXONOMY
Bibliographic record
Abstract
Ethical challenges that arise within healthcare delivery institutions are currently categorized as either clinical or organizational, based on the type of issue. Despite this common binary issue-based methodology, empirical study and increasing academic dialogue indicate that a clear line cannot easily be drawn between organizational and clinical ethics. Disagreement around end-of-life treatments, for example, often spawn value differences amongst parties at both organizational and clinical levels and requires a resolution to address both the case at hand and large-scale underlying system-level confounders. I refer to issues that contain elements of both clinical and organizational issues as hybrids and propose a new taxonomy to characterize hybrid cases. I contend that salient contextual features of an ethical issue, such as where it is identified, who it impacts and where it is ideally resolved in relation to its scope of impact, should inform procedure. Implementation of a Hybrid taxonomy viewing ethical issues as existing on a continuum furthers that end. The primary goals are to 1) systematize thinking about ethical issues that arise within healthcare delivery institutions and 2) allow the content of the ethical challenge to drive the process, rather than continuing to rely on the traditional binary issue-based choice. Failure to capture the complexity of hybrid situations perpetuates incomplete information and ultimately an inchoate resolution that creates more questions than answers.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".