How Community Nurses Manage Ethical Conflicts: A Grounded Theory Study
Bibliographic record
Abstract
Research is limited on how nurses in community settings manage ethical conflicts. To address this gap, we conducted a study to uncover the process of behaviors enacted by community nurses when experiencing ethical conflicts. Guided by Glaserian grounded theory, we developed a theoretical model (Moral Compassing) that enables us to explain the process how 24 community nurses managed challenging ethical situations. We discovered that the main concern with which nurses wrestle is moral uncertainty (“Should I be addressing what I think is a moral problem?”). Moral Compassing comprises processes that resolve this main concern by providing community nurses with the means to attain the moral agency necessary to decide to act or to decide not to act. The processes are undergoing a visceral reaction, self-talk, seeking validation, and mobilizing support for action or inaction. We also discovered that community nurses may experience continuing distress that we labeled moral residue.
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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 | 0.153 | 0.125 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.039 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".