Caring as Coercion: Exploring the Nurse's Role in Mandated Treatment
Bibliographic record
Abstract
When nurses work in environments that have overlapping medical, legal, institutional, social, and therapeutic priorities, nursing care can become an effective tool in advancing the competing goals of these multiple systems. During the provision of patient care, nurses manage the tensions inherent in the competing priorities of these different systems, and skillful nursing can have the effect of rendering these tensions invisible. This puts nurses in an ethically complex position, where on one hand, their humanizing empathy has the potential to improve the delivery and effect of mandated care yet, on the other hand, their skillfulness can render invisible the weaknesses in medicolegal structures. In this article, we present a composite case study as a vehicle to illustrate the way this dilemma manifests in day-to-day nursing interactions and explore the potential of microethics to inform the everyday decisions of nurses delivering care-as-coercion.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.028 | 0.061 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.008 | 0.008 |
| 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".