Nurses' tension-based ethical decision making in rural acute care settings
Bibliographic record
Abstract
BACKGROUND: Nurses in acute care are frequently involved in ethical decision making and experience a higher prevalence of ethical conflicts and dilemmas. Nurses in underresourced rural acute care settings also are likely to face unique ethical challenges. However, rarely have the particular contexts of these experiences in rural acute care settings been researched. A culture of silence and fear in small towns has made exploring these issues difficult. OBJECTIVES: To explore registered nurses' experiences of ethical issues and ethical decision making in rural acute care hospitals in northern Ontario, Canada. RESEARCH DESIGN: Guided by an interpretive descriptive approach, data were collected by two nurse researchers using in-depth, individual, and semistructured telephone interviews. Data were managed with NVivo v.11 and analyzed using inductive, comparative, thematic analyses. PARTICIPANTS AND RESEARCH CONTEXT: The participants were eight registered nurses working in two acute care hospitals in northern Ontario. ETHICAL CONSIDERATIONS: Ethical protocols were followed in accordance with ethics approval from the researchers' university and the hospitals. FINDINGS: Results identified four themes that culminated in the development of a quadruple helix ethical decision-making framework of power, trust, care, and fear. DISCUSSION AND CONCLUSION: The participants described complex ethical conflicts and dilemmas in acute care settings that were influenced by the context of working and living in small rural communities in northern Ontario. Nurses described navigating ethics in practice using a tension-based approach to ethical decision making, needing to carry these issues silently and often having no resolution to ethical challenges. These findings have important implications for nursing education, research, and practice. Nurses need safe spaces, formal ethics support, and improved access to resources. Additional ethics education and training specific to the unique contexts of rural settings are needed.
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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 | 0.008 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.056 |
| Insufficient payload (model declined to judge) | 0.000 | 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; 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".