MétaCan
Menu
Back to cohort
Record W2901892631 · doi:10.2298/psi171120026m

Structure of self-schemas in patients with paranoia

2018· article· en· W2901892631 on OpenAlexaff
Ljiljana Mihić, Zdenka Novović, David J. A. Dozois, Richard P. Bentall, Tanja Petrović

Bibliographic record

VenuePsihologija · 2018
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsWestern University
FundersMinistarstvo Prosvete, Nauke i Tehnološkog Razvoja
KeywordsParanoiaPsychologySchema (genetic algorithms)Interpersonal communicationInterpersonal relationshipClinical psychologyDevelopmental psychologySocial psychologyPsychiatryInformation retrieval

Abstract

fetched live from OpenAlex

Negative self-schemas have been implicated in both paranoia and depression. There is a lack of research on the structural characteristics of self-schemas, even though these characteristics might be stable risk factors. The present study explored organization of positive and negative self-schemas in currently non-depressed individuals with persistent delusional disorder (PD), currently depressed individuals with persistent delusional disorder (PDD), and nonpsychiatric controls (NC). Self-schema consolidation was measured via the Psychological Distance Scaling Task. Within the interpersonal domain, negative selfschemas were more densely organized in PDD compared to both PD and NC. Both patient groups had less interconnected positive interpersonal schemas than controls. Within the achievement domain, PDD demonstrated less consolidated positive achievement schemas than NC and greater interconnectedness among negative adjectives than PD. Central limitation includes a small sample size. The findings point to an existence of at least two self-schema organizations in paranoid individuals.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.382
Teacher spread0.353 · 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 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

Citations3
Published2018
Admission routes1
Has abstractyes

Explore more

Same venuePsihologijaSame topicMental Health Research TopicsFrench-language works237,207