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Record W2211084416 · doi:10.1155/2015/296581

Subjective Memory Complaint and Depressive Symptoms among Older Adults in Portugal

2015· article· en· W2211084416 on OpenAlexaboutno aff
Mónica Sousa, Anabela Pereira, Rui Costa

Bibliographic record

VenueCurrent Gerontology and Geriatrics Research · 2015
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGeriatric Depression ScaleDepression (economics)Montreal Cognitive AssessmentDepressive symptomsClinical psychologyCognitionPsychiatryCognitive impairmentGerontology

Abstract

fetched live from OpenAlex

Background. Older adults report subjective memory complaints (SMCs) but whether these are related to depression remains controversial. In this study we investigated the relationship between the SMCs and depression and their predictors in a sample of old adults. Methods. This cross-sectional study enrolled 620 participants aged 55 to 96 years (74.04 ± 10.41). Outcome measures included a sociodemographic and clinical questionnaire, a SMC scale (QSM), a Geriatric Depression Scale (GDS), a Mini-Mental Status Examination (MMSE), and a Montreal Cognitive Assessment (MoCA). Results. The QSM mean total score for the main results suggests that SMCs are higher in old adults with depressed symptoms, comparatively to nondepressed old adults. The GDS scores were positively associated with QSM but negatively associated with education, MMSE, and MoCA. GDS scores predicted almost 63.4% of variance. Scores on QSM and MoCA are significantly predicted by depression symptomatology. Conclusion. Depression symptoms, lower education level, and older age may be crucial to the comprehension of SMCs. The present study suggested that depression might play a role in the SMCs of the older adults and its treatment should be considered.

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)0.001
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 score0.073
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.074
GPT teacher head0.403
Teacher spread0.329 · 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

Citations16
Published2015
Admission routes1
Has abstractyes

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