Social justice in Canadian nursing professional documents: A Foucauldian discourse analysis
Bibliographic record
Abstract
Social justice is widely advanced as a central nursing value, and yet conceptual understandings of social justice remain inconsistent and vague. Further, despite persistently articulated commitments to upholding social justice, the profession of nursing has been implicated in perpetuating inequities in health and health care. In this context, it is essential to establish both conceptual clarity and tangible guidance for nurses in enacting practices to advance social justice-particularly through regulatory, education and accreditation documents that shape the nursing profession. This Foucauldian discourse analysis examines how social justice is discursively positioned within nursing professional documents in Canada, and illustrates that social justice was largely discursively excluded from these texts. Where social justice discourses were invoked, we identified that four central discursive patterns obscured and de-centred this nursing value: (i) Vague language undermined professional commitments to social justice; (ii) Constructions of knowledge and awareness de-emphasized practice; (iii) Individualism discourses minimized institutional/professional responsibility; and (iv) Aspirational language obscured present action. Extending from this analysis, we contend that the nursing profession must re-examine how social justice is understood and articulated, and call for a re-conceptualization of social justice grounded in nursing practice toward remediating inequities in health and health care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.042 | 0.032 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".