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Record W4313430542 · doi:10.32920/21751490

Sanism, ‘Mental Health’, and Social Work/Education: A Review and Call to Action

2022· review· en· W4313430542 on OpenAlexaboutno aff
Jennifer Poole, Tania Jivraj, Araxi Arslanian, Kristen Bellows, Sheila Chiasson, Husnia Hakimy, Jessica Pasini, Jenna Reid

Bibliographic record

Venuenot available
Typereview
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsnot available
Fundersnot available
KeywordsOppressionMental healthWonderSocial workAction (physics)Call to actionSociologyPublic relationsWork (physics)Participatory action researchSocial psychologyPolitical sciencePsychologyLawPsychiatry

Abstract

fetched live from OpenAlex

<p>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. </p>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.639
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.007
Insufficient payload (model declined to judge)0.0080.000

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.

Opus teacher head0.483
GPT teacher head0.669
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations54
Published2022
Admission routes1
Has abstractyes

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