Bibliographic record
Abstract
A growing body of research has drawn attention to the hierarchical and bureaucratic nature of the hospital organizational environment in which nurses seek to resolve ethical problems related to patient care, whereas other studies have focused on the impact of nurses’ personal or professional qualities on those nurses’ ethical problem solving. This qualitative investigation sought to elucidate the extent to which nurses perceived their personal or professional qualities, as well as organizational characteristics, as influencing their ethical decision making. This investigator interviewed 10 registered nurses in 2 acute-care hospitals that were different in size, location, and type. A relational ethics lens assisted in the analysis of the data, emphasizing ways in which the nurses’ ethical problem solving was socially situated within a complex of relationships with others, including patients, families, physicians, and coworkers. Data analysis revealed key themes, including the nurses’ concern for patients, professional experience, layered relationships with others, interactions within the organization, and situational analysis of contexts and relationships. Subthemes included the nurses’ relationships with patients, physicians, patients’ families, and coworkers. This study revealed a range of ethical problems. Nurses saw their patients as their greatest concern; the nurses worked within a social context of multilayered and complex relationships within a hierarchical, bureaucratic organization with the desire to bring about the best outcomes for patients. The participants described ethical concerns related to the actions or decisions of physicians, patients’ family members, and nurses’ coworkers. The nurses’ deliberation to resolve these ethical problems considered risks and benefits for patients, nurses, and others. The nurses seemed to carry out a contextual assessment, analyzing the presence of mutual respect, the extent of relational engagement, and the potential for opening relational space in order to work together with others to resolve the ethical problem for the patient’s best outcome. The nurses’ ethical actions were socially situated within this complex interpersonal context. This thesis discusses implications of these findings for nursing research, education, and practice.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.056 | 0.021 |
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".