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Record W7084619819 · doi:10.59075/ijss.v3i1.1940

Role of Positive-Negative Symptoms of Schizophrenia on Motivation and Cognitive Problems

2025· article· en· W7084619819 on OpenAlexaboutno aff

Bibliographic record

VenueIndus journal of social sciences. · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicRemote Sensing in Agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsSchizophrenia (object-oriented programming)CognitionCognitive remediation therapyRating scaleCognitive skillSituational ethicsPsychological intervention

Abstract

fetched live from OpenAlex

Investigating positive-negative symptoms, motivation, and cognitive functioning in individuals with schizophrenia was the aim of the current study. In this cross-sectional study, 70 outpatients with schizophrenia who were 19 years of age or older participated. Every participant finished the Montreal Cognitive Assessment Scale (MoCA), the Situational Motivation Scale (SIMS), and the Brief Psychiatric Rating Scale (BPRS-4.0). The hypothesis that positive symptoms would boost motivation and have no effect on cognitive functioning in schizophrenia patients while negative symptoms would lower motivation and cognitive functioning was examined using linear regression. The results reflected that positive symptoms significantly increase the motivation and had no effect on cognitive functioning in schizophrenic patients. The results also have revealed that negative symptoms significantly decrease the motivation patients whereas it was a non-significantly predictor of cognitive functioning in schizophrenic patients. These findings might help in development and providing interventions to schizophrenic dealing with problems with motivation and cognitive functioning.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.009
GPT teacher head0.241
Teacher spread0.232 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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