Understanding how patient’s personal value systems challenge nurses’ views when providing care
Bibliographic record
Abstract
Background: Personal conflict as a result of differences in value systems may be a moral challenge faced by nurses when caring for individuals with different cultures and beliefs than their own and is an area of limited study. As nurses care for patients with diverse backgrounds, it is inevitable that there may be differences in values and that these moral conflicts could be distressing. This type of moral distress can cause nurse’s personal conflict as their values and beliefs may not match those of their patients.Methods: Moustakas (1994) phenomenological approach was used to elicit meaning from nurse’s stories regarding morally challenging situations. Ten nurses were solicited, but only two wrote about their experiences; one worked in a busy emergency department and the other in a rural community setting.Results: Four themes were identified: differences, moral code, weight of the transgression, and internal resolution. Value system conflict was the emergent constituent when nurses shared their stories of caring for patients with different values and beliefs than their own.Conclusions: Nurses may be challenged to care for patients who have different cultural practices than themselves. Value system conflicts may cause strife for nurses who have different beliefs than the patients they care for. Interventions that support strategies to mitigate moral distress such as simulation could be included in future research. Use of simulation as a mechanism for training can assist nurses to work through morally challenging situations.
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| 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".