Applying a Foucauldian lens to the Canadian code of ethics for registered nurses as a discursive mechanism for nurses professional identity
Bibliographic record
Abstract
This study examines the Canadian Code of Ethics for Registered Nurses as a discursive mechanism for shaping nurses' professional identity using a Foucauldian lens. Nurses are considered essential in healthcare, yet the nursing profession has struggled to be recognized for its discipline-specific knowledge and expertise and, as such, has remained the subject of and subject to the dominant discourses within healthcare and society generally. Developing a professional identity in nursing begins after the necessary education and training are achieved and embodies the profession's history, values, code of ethics, and expectations of the profession that distinguish it from other professions. Since nurses' professional identity is shaped through discourse, it raises the question of whether there are spaces to reconceptualize nurses' subject position within health care. Since professional identity is considered the embodiment of knowledge and practice, the code of ethics bears examination both for its effect on nurses' professional identity and as a potential site from which to challenge hegemonic assumptions. This article discusses the concept of professional identity in nursing and its development through the discursive formations in the code of ethics. The sources of power/knowledge are examined as both mechanisms of control and as spaces for change.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.023 | 0.077 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".