Centering Social Justice in Mental Health Practice: Epistemic Justice and Social Work Practice
Bibliographic record
Abstract
This article examines how mental health social work practice can move outside the hegemony of the medical model using approaches that honor the centering of social justice. By using the philosophical analysis of epistemic injustice and the ethics of knowing, I move out of the traditional psychiatric and psychological conceptual frameworks and discuss new guiding principles for practice. In the context of the radical tradition in social work and the impetus to blend theory with practice, I consider the use of narrative and anti-oppressive approaches to center social justice principles in individual dyadic work as well as in wider systems family and community work and policy advocacy. I evaluate these approaches through the principles of epistemic justice and discuss the importance of a relational collaborative approach where honoring the client and exploring lived experience are central to both the concepts of testimonial justice, hermeneutic justice and anti-oppressive practice.
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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 | 0.043 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.024 | 0.158 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.003 | 0.036 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".