Development of a Social Justice Gauge and Its Use to Review the Canadian Nurses Associationʼs Code of Ethics for Registered Nurses
Bibliographic record
Abstract
In Brief Betty Bekemeier and Patricia Butterfield undertook a critical review of 3 American nursing documents in relation to the concept of social justice. Their article inspired a review of the Canadian Code of Ethics for Registered Nurses, using a Social Justice Gauge developed by the Canadian Nurses Association. The article outlines the development of the gauge and its use in this review. Although some evidence of generic and outdated language is evident in the Canadian code, the text appears well aligned with social justice ideals overall. That being said however, there still remains significant possibility for enlarging the application of social justice, especially in relation to the place of nurses in healthcare institutions and in nontraditional nursing settings, in future revisions of the code. Work to further examine, adapt, and test the Canadian Nurses Association's Social Justice Gauge is encouraged. Bekemeier and Butterfield's critical review of three American nursing documents in relation to the concept of social justice inspired a review of the Canadian Code of Ethics of Registered Nurses. While the text appears well aligned with social justice ideals, there still remains significant possibility for enlarging the application of social justice, especially in relation to the place of nurses in healthcare institutions and in nontraditional nursing settings.
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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 | 0.131 | 0.246 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.035 | 0.029 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".