The Subaltern: Illuminating matters of representation and agency in mental health nursing through a postcolonial feminist lens
Bibliographic record
Abstract
Inpatient mental health nursing operates with an overarching goal to support people living with mental health challenges by managing risk of harm to self and others, decreasing symptoms, and promoting capacity to live outside of hospital settings. Yet, dominant, harmful stereotypes persist, constructing patients as less than, in need of saving, and lacking self-control and agency. These dominant assumptions are deeply entrenched in racist, patriarchal, and Othering beliefs and continue to perpetuate and (re)produce inequities, specifically for people with multiple intersecting identities relating to race, class, gender, and culture. This paper explores the relevance of postcolonial feminism, particularly Gayatri Spivak's concept of Subaltern-conceptualized as groups of people who are denied access to power and therefore continue to be systematically oppressed and marginalized-in illuminating the problematic and dominant assumptions about people living with mental health challenges as lacking agency and requiring representation. Through an understanding of Subalternity, this paper aims to decenter and deconstruct dominant colonial, patriarchal narratives in mental health nursing, and ultimately calls for mental health nursing to fundamentally reconsider prevailing assumptions of patients as needing representation and lacking agency.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.114 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 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".