Dare we speak of ethics? Attending to the unsayable amongst nurse leaders
Bibliographic record
Abstract
There is increasing emphasis on the need for collaboration between practice and academic leaders in health care research. However, many problems can arise owing to differences between academic and clinical goals and timelines. In order for research to move forward it is important to name and address these issues early in a project. In this article we use an example of a participatory action research study of ethical practice in nursing to highlight some of the issues that are not frequently discussed and we identify the impact of things not-named. Further, we offer our insights to others who wish to be partners in research between academic and practice settings. These findings have wide implications for ameliorating misunderstandings that may develop between nurse leaders in light of collaborative research, as well as for participatory action research.
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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 | 0.068 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.059 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.009 | 0.022 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".