Resistance for Disrupting Inequities and Fostering Social Justice in Nursing: A Discussion
Bibliographic record
Abstract
AIMS: The purpose of this paper is to provide a conceptual overview of resistance and argue for the need to embrace resistance as a part of nurses' professional repertoire for disrupting inequities and fostering social justice in both nursing education and practice. DESIGN: Discursive article. DATA SOURCES: Published peer reviewed literature on 'resistance' and 'professional resistance' in nursing, medicine, social work and other allied health care professions. RESULTS: Enhancing critical consciousness and engaging in intersectional collaboration are promising strategies to embrace resistance for collective action towards disrupting inequities and injustices in nursing education and practice. CONCLUSION: Embracing and legitimising resistance in everyday individual and social interactions in educational and practice settings is instrumental to fostering social justice in nursing. Without resistance, nurses may risk jeopardising enactment of moral and ethical responsibilities and suppressing their professional values of caring and compassion. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Nurses can embrace resistance in practice to counteract social injustice and promote diversity, equity, inclusion and belonging and antiracism in clinical and educational settings. IMPACT: Research demonstrated that perceived and real inequities and injustices are common in nursing in the form of individual and structural racism, sex and gender discrimination, power imbalances and incivility. Nurses' engagement in resistance and increased capacity to resist injustices and incivilities can play an instrumental role in disrupting professional inequities in clinical practice and education. PATIENT AND PUBLIC CONTRIBUTION: There was no patient or public involvement in the design or writing of this discursive article.
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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 | 0.021 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.040 |
| Scholarly communication | 0.017 | 0.028 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".