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Record W4380368660 · doi:10.18061/dsq.v42i3-4.7808

Patient Resistance to Psychiatric Discourse and Power

2023· article· en· W4380368660 on OpenAlexaffabout
Matthew S. Johnston, Matthew D. Sanscartier, Rhys Steckle

Bibliographic record

VenueDisability Studies Quarterly · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsCarleton UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsResistance (ecology)Power (physics)Mental healthService (business)AgoraControl (management)PsychiatrySociologyInternet privacyPsychologyPublic relationsPolitical scienceBusinessComputer science

Abstract

fetched live from OpenAlex

Drawing on 5090 English reviews of 486 psychiatrists working in Canada posted on ratemds.com, this study explores how mental health service users refuse to become subjectivized by psychiatric discourse and power. We interrogate how digital mediums provide mental health service users with a community of critique to regain control over settings where there are many power imbalances. We argue that websites like ratemds.com act as a digital agora in which people are afforded the ability to make the personal political. Through critiquing their own doctors, mental health service users invert the question of what is “wrong” with them to what is “wrong” with agents of the psychiatric apparatus. By regaining a say over their treatment/conditions and insisting doctors are asking the wrong questions to better control their identities, service users refuse to accept the diagnoses, pathologies, and practices imposed on them. We discuss how their transgression in this forum provides new insights into psychiatric resistance that is of special interest to scholars and service users positioned in the Mad Studies movement.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.144
GPT teacher head0.469
Teacher spread0.324 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2023
Admission routes2
Has abstractyes

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