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Record W2603365733 · doi:10.1093/schbul/sbx023.029

SA30. Self-Assessment of Amotivation and Insight into Patients With Schizophrenia

2017· article· en· W2603365733 on OpenAlexaboutno aff
Oleg Papsuev, Larisa Movina, M. Minyaycheva, Lauren Luther

Bibliographic record

VenueSchizophrenia Bulletin · 2017
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsnot available
Fundersnot available
KeywordsAmotivationApathyPositive and Negative Syndrome ScaleNeurocognitiveSchizophrenia (object-oriented programming)PsychologyClinical psychologyCognitionNeuropsychologyPsychiatryPsychosisIntrinsic motivation

Abstract

fetched live from OpenAlex

Background: Schizophrenia is a disabling disorder characterized by negative and cognitive symptoms. The negative symptom domain of low motivation has recently been found to be an important determinant of functioning. Currently, motivation is frequently assessed with either self-rated or clinician-rated motivation measures. However, little is known about the overlap between self-rated and clinician-rated motivation and whether these two assessment types are differentially related to clinical variables. Therefore, this study investigated (1) the association between self-rated and clinician-rated motivation, (2) the clinical correlates of both motivation assessment types, and (3) the correlates of the discrepancy between the motivation assessments types. Methods: Fifty patients with schizophrenia spectrum disorders were assessed by trained clinicians using the Positive and Negative Syndrome Scale (PANSS), the Calgary Depression Scale (CDSS), and both the clinician-rated (C) and self-rated (S) versions of the Apathy Evaluation Scale (AES). Neurocognition was assessed with the Brief Assessment of Cognition in Schizophrenia (BACS). Social cognition was assessed with the Hinting Task, the Relationships Across Domains measure, and the Ekman-60 emotion recognition task. Results: The AES-C and AES-S were positively correlated (r = .43; P < .05). Further, moderate, positive correlations were established between the AES-C and most of the PANSS amotivation subscale items (N2 (r = .51), N4 (r = .45)). However, a significant correlation between the AES-C and the G16 item of the PANSS amotivation subscale was not observed. The AES-S was not significantly correlated with any of the PANSS amotivation items. The AES-C did not correlate with the PANSS depression item or the CDSS total score, while moderate correlations with the AES-S were observed with both (r = .38 and r = .45, respectively). The AES-C/AES-S discrepancy score was positively correlated with the PANSS insight item (r = 0.39) and the presence of a paranoid schizophrenia diagnosis (r = .32). No significant correlations were observed between the discrepancy score and the BACS, social cognition measures, or additional demographic variables. Conclusion: While the clinician-rated AES is regarded as a sensitive instrument for the assessment of apathetic/amotivation schizophrenia symptoms, our results suggest that scores from the self-rated AES need to be interpreted carefully. Our findings also indicate that patients with schizophrenia might be less aware of primary negative (i.e., amotivation) symptoms, and when asked to self-rate negative symptoms, they rate secondary negative symptoms caused by depression. Results also suggest that reduced insight might be driving part of the discrepancy between self-rated and clinician-rated motivation. Findings should be considered when choosing motivation measures.

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)0.004
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.274
Teacher spread0.264 · 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".

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Citations1
Published2017
Admission routes1
Has abstractyes

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