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Record W3135866117 · doi:10.1080/17522439.2021.1876159

A “blip in the road”: experiences of identity after a first episode of psychosis

2021· article· en· W3135866117 on OpenAlexafffund
Phoebe Friesen, Jordan Goldstein, Lisa B. Dixon

Bibliographic record

VenuePsychosis · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of CanadaGraduate Center
KeywordsPsychologyPsychosisIdentity (music)PsychoanalysisPsychiatryArtAesthetics

Abstract

fetched live from OpenAlex

Introduction: Psychosis can affect identity in fundamental ways. Increasingly, those experiencing psychosis for the first time are enrolled in early intervention services. We sought to explore how individuals enrolled in such services felt their identity was impacted by their experience of psychosis.Methods: In-depth interviews exploring themes related to identity and psychosis were conducted with 10 participants from two early intervention services in New York City.Findings: The experience of psychosis alienated many participants from themselves, although participants differed in whether these experiences were meaningful to their self-understanding. Participants also varied in how they sought to explain their experiences of psychosis; some participants questioned their diagnoses and the explanations offered to them, whereas others tried to negotiate between a clinical description of psychosis and their own understanding of their experiences. Many participants also experienced positive changes following their experience of psychosis, including greater maturity, empathy, and compassion.Discussion: Some participants appeared to take on recovery styles of both integrating and sealing-over in response to their experience of psychosis, while most participants’ reports were suggestive of post-traumatic growth. Several struggled to make sense of the explanatory frameworks offered to them, drawing from various explanatory frameworks in a form of bricolage.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.995

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.035
GPT teacher head0.303
Teacher spread0.268 · 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 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

Citations3
Published2021
Admission routes2
Has abstractyes

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