Into the Grey Zone: Retired Nurses’ Reflections on Ethics in Canadian Nursing Practice
Bibliographic record
Abstract
Context: Nurses are often hesitant to talk about ethical issues in their practice for many unique and valid reasons. What if the burden of risk was lifted upon retirement, even if just slightly? The purpose of this study was to explore retired nurses’ reflections on their experiences of ethical issues and decision making in various nursing practice settings throughout their careers and to glean recommendations for ethics in contemporary nursing practice. Methods: Data were collected via in-depth, individual, semi-structured interviews. Guided by an interpretive, descriptive approach, data were managed with NVivo v.11 and analyzed with an inductive, comparative, thematic approach. In northern Ontario, two nurse researchers co-interviewed eight retired nurses with decades of practice experience across diverse Canadian health care settings. Ethics approval was obtained through Lakehead University’s Research Ethics Board. Findings: Three themes emerged to address ethical issues in practice; these are creativity, resourcefulness, and a strong sense of community with other nurses. Further, the retired nurses’ collated reflections on ethics in practice are presented as the FIG model: Fellowship, Ingenuity, and Gumption. Conclusions: This study identifies ethical underpinnings that retired nurses have used to effectively respond to ethical issues in their practice. Those who are currently nursing, and nursing as a profession, may wish to recognize and retain these strategies in order to continue to deliver a high standard of quality, ethical care. Recommendations for practice, research, and education are offered.
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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 | 0.024 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.046 | 0.028 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".