Sanism, ‘Mental Health’, and Social Work/Education: A Review and Call to Action
Bibliographic record
Abstract
Sanism is a devastating form of oppression, often leading to negative stereotyping or arguments that individuals with ‘mental health’ histories are not fit to study social work. However, the term sanism is rarely used, understood, or interrogated in the social work academy, even in anti-oppressive spaces. Indeed, social work has been so loyal to the medical model that sanist aggressions, such as pathologizing, labelling, exclusion, and dismissal have become a ‘normal’ part of professional practice and education. We query the moral integrity of a profession that at its foundational core could play a role in such a discriminatory tactic as sanism. We wonder what the effect of this has been on social work and its education. We ask, who has been excluded, what has been silenced or denied because of the privileging of medical conceptualizations of madness, and how can we work toward anti-sanist social work today? In this paper we provide an overview of sanism. We offer a more critical review of the literature on ‘mental health’ and social work. We report on our anti-sanist participatory pilot research, and aligned with current Canadian rights work, we call for action with respect to how social workers theorize, research, and respond to madness now.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".