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Record W7132914389

Cognitive Discrepancies, Values and Subjective Well-Being in People with Schizophrenia

2020· dissertation· W7132914389 on OpenAlexfundno aff
Daniel Krzyzanowski

Bibliographic record

VenueTSpace · 2020
Typedissertation
Language
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
FundersMacEwan University
KeywordsDisengagement theorySchizophrenia (object-oriented programming)Schizoaffective disorderCognitionContext (archaeology)Openness to experienceSocial functioningDiagnosis of schizophrenia
DOInot available

Abstract

fetched live from OpenAlex

Past research indicates that people with schizophrenia often achieve similar levels of subjective well-being (SWB) compared to healthy individuals despite prominent symptomatology and significant functional, social and cognitive difficulties. People with schizophrenia also report more conservative value systems (less openness to change and greater emphasis on tradition), suggesting that changing motivations and personal values may contribute to SWB and the apparent motivational deficits commonly reported in this population. In the current study, middle-aged people with schizophrenia or schizoaffective disorder (n=29) and community control participants (n=23) rated their current SWB, life satisfaction, hope, and values. They also completed a battery of cognitive tests and diagnostic interviews. Patients reported similar levels of SWB in the context of significant cognitive, social and functional difficulties, more conservative value systems, and a greater propensity for goal disengagement compared to controls. These results are discussed in relation to lifespan development and motivational theory.

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)0.002
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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.012
GPT teacher head0.318
Teacher spread0.306 · 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
Published2020
Admission routes1
Has abstractyes

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