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Record W2794536653 · doi:10.1093/schbul/sby018.901

S114. ASSOCIATION BETWEEN APATHY AND DEPRESSION: SECONDARY OR REFLECTING UNDERLYING COMMON FEATURES?

2018· article· en· W2794536653 on OpenAlexaboutno aff
Ann Færden, Siv Hege Lyngstad, Ingrid Melle

Bibliographic record

VenueSchizophrenia Bulletin · 2018
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsApathyDepression (economics)Clinical psychologyPsychiatryPsychologyPsychosisSchizophrenia (object-oriented programming)FeelingAssociation (psychology)MedicineCognitionPsychotherapist

Abstract

fetched live from OpenAlex

Negative symptoms and depression have in many studies been found to be moderately associated, and when present, it reflects negative symptoms of a secondary nature. Primary negative symptoms are thought to be intrinsic to schizophrenia, while secondary to be caused by depression, positive symptoms and medication side effects. Instability in above associations over time is also considered to reflect a secondary origin. Despite the clinical importance of secondary negative symptoms little research has focused on what is their underlying nature. Especially apathy and depression have common clinical features and are often found to correlate. Apathy and depression are self-perceived states and self-reports could add to our understanding. Studying the underlying themes of associations between apathy and depression as well as stability over time could therefore add to the current understanding of the primary or secondary nature of negative symptoms. Eighty-four first episode psychosis patients from TOP/NORMENT study in Oslo, Norway were assessed at baseline and 1-year follow-up with the Calgary Depression Scale (CDSS), Apathy Evaluation Scale (AES), both self-report (AES-S) and clinician (AES-C), and the Positive and Negative Symptoms Scale (PANSS). Correlation with total scale, individual scale items and linear regression was used to study associations and explained variance over time. Results were repeated controlling for positive symptoms and excluding those with high level of depression. CDSS and AES correlated at the 0.4 to 0.5 levels at both baseline and follow up, regardless of AES-S or AES-C. Hopelessness and feeling of depression were the CDSS items with stable and concurrent correlation strength to AES-S and AES-C. For CDSS, we found correlation of equal strength and stability to the AES-S- and AES-C items of getting things done during the day, spending time on interests, getting excited and taking initiative. Same significant correlations to CDSS were found for PANSS amotivation factor, but not for PANSS expressive factor. Controlling for PANSS positive symptoms did not change results, and excluding those with high levels of depression only mildly changed results. This study shows a significant correlation between apathy and depression that is stable over time for the full scale and also at the item level, regardless of self-reporting or clinician assessed apathy. Underlying themes of the concurrent correlation reflect lack of initiative and hopelessness and are in line with the defeatist beliefs found to correlate with negative symptoms, mediate between motivation and reduced effort and have recently been a target for cognitive remediation therapy. This study does not give an answer to a primary or secondary origin of apathy, but the stability points more to an underlying common nature than one being the cause of the other.

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 score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0200.001

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 head0.048
GPT teacher head0.351
Teacher spread0.302 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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