MétaCan
Menu
Back to cohort
Record W1573342946 · doi:10.1177/0020764015584647

Boredom proneness predicts quality of life in outpatients diagnosed with schizophrenia-spectrum disorders

2015· article· en· W1573342946 on OpenAlexaff
Cory Gerritsen, John D. Eastwood

Bibliographic record

VenueInternational Journal of Social Psychiatry · 2015
Typearticle
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsYork University
Fundersnot available
KeywordsBoredomSchizophrenia spectrumSchizophrenia (object-oriented programming)Quality of life (healthcare)PsychologyClinical psychologyPsychiatryMedicinePsychosisPsychotherapist

Abstract

fetched live from OpenAlex

BACKGROUND: There is increasing recognition of the clinical significance of boredom associated with functional impairments in schizophrenia. Previous work has highlighted the importance of motivational deficits more broadly, although no study has yet explored the unique effects of boredom on community outcomes. AIMS: This study aims to measure boredom proneness among outpatients diagnosed with schizophrenia to determine whether it is elevated in this population and to determine its relation to quality-of-life outcomes. METHODS: A self-report measure of boredom proneness along with standard measures of symptoms and functional status was administered to a community-dwelling sample of schizophrenia outpatients. RESULTS: Boredom proneness was found to be elevated in this population and was associated with reduced quality of life, specifically with leisure activity dissatisfaction and reduced sense of financial well-being. Negative symptoms were determined to be associated with reduced work and school functioning. CONCLUSION: This pattern of unique effects on quality of life highlights the clinical relevance of identifying a subjective state of boredom and has theoretical importance in distinguishing boredom proneness specifically from more general avolitional and amotivational conditions that have tended to be the focus of clinical observation and previous research.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.331

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)0.0000.000

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 head0.037
GPT teacher head0.314
Teacher spread0.276 · 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 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

Citations22
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Social PsychiatrySame topicMind wandering and attentionFrench-language works237,207