Nurses’ narratives of moral identity: Making a difference and reciprocal holding
Bibliographic record
Abstract
BACKGROUND: Explicating nurses' moral identities is important given the powerful influence moral identity has on the capacity to exercise moral agency. RESEARCH OBJECTIVES: The purpose of this study was to explore how nurses narrate their moral identity through their understanding of their work. An additional purpose was to understand how these moral identities are held in the social space that nurses occupy. RESEARCH DESIGN: The Registered Nurse Journal, a bimonthly publication of the Registered Nurses' Association of Ontario, Canada, features a regular column entitled, 'In the End … What Nursing Means to Me …' These short narratives generally include a story of an important moment in the careers of the authors that defined their identities as nurses. All 29 narratives published before June 2015 were analysed using a critical narrative approach, informed by the work of Margaret Urban Walker and Hilde Lindemann, to identify a typology of moral identity. Ethical considerations: Ethics approval was not required because the narratives are publicly available. FINDINGS: Two narrative types were identified that represent the moral identities of nurses as expressed through their work: (1) making a difference in the lives of individuals and communities and (2) holding the identities of vulnerable individuals. DISCUSSION: Nurses' moral identities became evident when they could see improvement in the health of patients or communities or when they could maintain the identity of their patients despite the disruptive forces of illness and hospitalization. In reciprocal fashion, the responses of their patients, including expressions of gratitude, served to hold the moral identities of these nurses. CONCLUSION: Ultimately, the sustainability of nurses' moral identities may be dependent on the recognition of their own needs for professional satisfaction and care in ways that go beyond the kind of acknowledgement that patients can offer.
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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 | 0.025 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.027 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| 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".