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Record W2127304330 · doi:10.1177/1049732315578403

Creativity and Bipolar Disorder

2015· article· en· W2127304330 on OpenAlexafffund
Sheri L. Johnson, Michelle Moezpoor, Greg Murray, Rachelle Hole, Steven J. Barnes, Erin E. Michalak

Bibliographic record

VenueQualitative Health Research · 2015
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsCreativityPsychologyBipolar disorderManiaThematic analysisHypomaniaQualitative researchClinical psychologyDevelopmental psychologySocial psychologyMoodSociology

Abstract

fetched live from OpenAlex

Bipolar disorder (BD) has been related to heightened creativity, yet core questions remain unaddressed about this association. We used qualitative methods to investigate how highly creative individuals with BD understand the role of symptoms and treatment in their creativity, and possible mechanisms underpinning this link. Twenty-two individuals self-identified as highly creative and living with BD took part in focus groups and completed quantitative measures of symptoms, quality of life (QoL), and creativity. Using thematic analysis, five themes emerged: the pros and cons of mania for creativity, benefits of altered thinking, the relationship between creativity and medication, creativity as central to one's identity, and creativity's importance in stigma reduction and treatment. Despite reliance on a small sample who self-identified as having BD, findings shed light on previously mixed results regarding the influence of mania and treatment and suggest new directions for the study of mechanisms driving the creative advantage in BD.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.596
GPT teacher head0.641
Teacher spread0.045 · 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 source (direct Gemma or distilled Codex), 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

Citations21
Published2015
Admission routes2
Has abstractyes

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