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Record W4410014303 · doi:10.1016/j.ssmmh.2025.100454

Out of Focus: Illuminating embodied entanglements of treatment resistance in schizophrenia and depression through narrative interviews and digital storytelling

2025· article· en· W4410014303 on OpenAlexafffund
Leighton Schreyer, Allison Crawford, Oshan Fernando, Vanessa Lockwood, Sophie Soklaridis, Araba Chintoh, Suze Berkhout, Csilla Kalocsai

Bibliographic record

VenueSSM - Mental Health · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreCentre for Addiction and Mental HealthPublic Health OntarioUniversity Health NetworkUniversity of Toronto
FundersTemerty Faculty of Medicine, University of Toronto
KeywordsEmbodied cognitionNarrativeStorytellingResistance (ecology)Focus (optics)Schizophrenia (object-oriented programming)PsychologyDigital storytellingPsychoanalysisArtEpistemologyPsychiatryPhilosophyLiteratureBiologyPedagogyPhysicsOptics

Abstract

fetched live from OpenAlex

• Embodied dimensions of treatment resistance (TR) are challenging to delineate. • A diffractive methodology can help generate different ways of knowing. • Digital storytelling is a valuable tool to complicate illness narratives. • Lived experiences of TR traverse conventional notions of a unitary self and time. • TR becomes intelligible through intra-actions and entanglements.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

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

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 head0.115
GPT teacher head0.428
Teacher spread0.314 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2025
Admission routes2
Has abstractyes

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