Sustaining hope as a moral competency in the context of aggressive care
Bibliographic record
Abstract
BACKGROUND: Nurses who provide aggressive care often experience the ethical challenge of needing to preserve the hope of seriously ill patients and their families without providing false hope. RESEARCH OBJECTIVES: The purpose of this inquiry was to explore nurses' moral competence related to fostering hope in patients and their families within the context of aggressive technological care. A secondary purpose was to understand how this competence is shaped by the social-moral space of nurses' work in order to capture how competencies may reflect an adaptation to a less than ideal work environment. RESEARCH DESIGN: A critical qualitative approach was used. PARTICIPANTS: Fifteen graduate nursing students from various practice areas participated. ETHICAL CONSIDERATIONS: After receiving ethics approval from the university, signed informed consent was obtained from participants before they were interviewed. FINDINGS: One overarching theme 'Mediating the tension between providing false hope and destroying hope within biomedicine' along with three subthemes, including 'Reimagining hopeful possibilities', 'Exercising caution within the social-moral space of nursing' and 'Maintaining nurses' own hope', was identified, which represents specific aspects of this moral competency. DISCUSSION: This competency represents a complex, nuanced and multi-layered set of skills in which nurses must be well attuned to the needs and emotions of their patients and families, have the foresight to imagine possible future hopes, be able to acknowledge death, have advanced interpersonal skills, maintain their own hope and ideally have the capacity to challenge those around them when the provision of aggressive care is a form of providing false hope. CONCLUSION: The articulation of moral competencies may support the development of nursing ethics curricula to prepare future nurses in a way that is sensitive to the characteristics of actual practice settings.
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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 | 0.012 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.015 |
| 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; a candidate call from one teacher head, 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".