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Record W2122367478 · doi:10.1080/13648470.2010.493604

Self-compliance at ‘Prozac campus’

2010· article· en· W2122367478 on OpenAlexafffund
Kelly McKinney, Brian G. Greenfield

Bibliographic record

VenueAnthropology and Medicine · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsMcGill UniversityMontreal Children's HospitalJohn Abbott College
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGovernmentalityCompliance (psychology)Unconscious mindExperiential learningMedical anthropologyEmpowermentPower (physics)Meaning (existential)Interpretation (philosophy)SociologyPsychiatric medicationEthnographyPsychologyPublic relationsSocial psychologyPsychiatryPsychotherapistSocial sciencePoliticsPedagogyMental healthPolitical science

Abstract

fetched live from OpenAlex

This paper focuses on psychiatric medication experiences among a sample of North American university students to explore a new cultural and social landscape of medication 'compliance.' In this landscape, patients assume significant personal decision-making power in terms of dosages, when to discontinue use and even what medications to take. Patients carefully monitor and regulate their moods, and actively gather and circulate newly legitimated blends of expert and experiential knowledge about psychiatric medications among peers, family members and their physicians. The medications too, take a vital role in shaping this landscape, and help to create the spaces for meaning-making and interpretation described and explored in this article. In concluding the article, the authors claim that two popular academic discourses in medical anthropology, one of patient empowerment and shared decision-making and the other of technologies of self and governmentality, may fail to account for other orders of reality that this paper describes - orders shaped and influenced by unconscious, unexpressed and symbolic motivations.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.896
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0200.000

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 head0.037
GPT teacher head0.341
Teacher spread0.304 · 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
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

Citations19
Published2010
Admission routes2
Has abstractyes

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