The practice of nursing research: getting ready for ‘ethics’ and the matter of character
Bibliographic record
Abstract
Few would argue with the idea that nursing research should be conducted ethically yet obtaining ethical approval is considered by many to have become unnecessarily burdensome. This brief article investigates the idea that there might be a relationship between the level of perceived burdensomeness of the research ethics application process on the one hand and the character of the nurse-researcher on the other. Given that nurses are required to be other-regarding, a nurse who undertakes research primarily for self-regarding reasons would seem to be acting in ways inconsistent with the aims of nursing as set out in nursing codes. It is suggested that the self-regarding nurse-researcher may find the ethics application process more burdensome than the other-regarding nurse-researcher who, it is further suggested, is engaged with nursing research as a practice in the technical sense in which that term has been developed by the philosopher Alasdair MacIntyre.
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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 | 0.101 | 0.180 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.008 |
| 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; both teacher heads agree on what is shown here.
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".