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Record W4406906267 · doi:10.36838/v6i9.14

How Does Unipolar Depression Influence Memory Encoding? —— The Role of Cognitive Support in Depression Patients

2024· article· en· W4406906267 on OpenAlexaff
Xinyi Lu

Bibliographic record

VenueInternational journal of high school research · 2024
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsBritish Columbia Institute of Technology
Fundersnot available
KeywordsDepression (economics)Encoding (memory)PsychologyCognitionCognitive psychologyClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

MDD often brings about numerous health challenges and disruptions, with memory loss emerging as a prominent concern.Many studies have tried to find the relationship between MDD and the patients' memory functions.Research has shown that depression affects memory encoding in a variety of significant ways.This paper analyzes research articles and relevant literature from 2000 to 2023 to connect the effects of depression to symptoms of memory loss.The included studies cover the effects on various types of memory encoding for patients either under depression or in remitted depression throughout differing age cohorts.Research methods include testing on source memory, RCFT (Rey-Osterrieth-Complex-Figure-Test), and learning and recall.Overall, depression leads to many negative effects on retention even when a patient is in remission including deficits in contextual cognitive memory, both verbal and non-verbal memory, and working memory.Depression patients have difficulties during the encoding process, which causes impairments of these memories.Giving patients extra cognitive support can help lessen the deficits.However, the problem alters with aging, memory kinds, the number of past depressive episodes, and other control factors.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.055
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.030
GPT teacher head0.422
Teacher spread0.392 · 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
Published2024
Admission routes1
Has abstractyes

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