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Record W2321016699 · doi:10.2310/7750.2013.13167

Association between Skin Conditions and Depressive Disorders in Community-Dwelling Older Adults

2014· article· en· W2321016699 on OpenAlexafffundabout
Samantha Gontijo Guerra, Michel Préville, Helen‐Maria Vasiliadis, Djamal Berbiche

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2014
Typearticle
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsHôpital Charles-Le MoyneUniversité de Sherbrooke
FundersCanadian Institutes of Health Research
KeywordsMedicineAssociation (psychology)GerontologyDermatology

Abstract

fetched live from OpenAlex

BACKGROUND: Depression is frequently observed in dermatologic patients. However, the association between depressive disorders and skin conditions has rarely been explored through population-based studies, especially within older-adult populations. OBJECTIVE: To test this association in a representative sample of an older-adult population. METHODS: Data came from the Survey on the Health of the Elderly (Enquête sur la Santé des Aìnés [ESA]), a longitudinal survey conducted in Quebec among 2,811 older adults. Cross-lagged panel models were used to simultaneously examine cross-sectional and longitudinal relationships between the presence of skin conditions and depressive disorders. RESULTS: The prevalence of skin conditions was 13%, and the prevalence of depressive disorders among participants presenting with skin conditions was 11%. Our results indicated significant cross-sectional correlation (ζ = 0.20) between skin conditions and depressive disorders, but no longitudinal association was observed. CONCLUSION: Our results reinforce the hypothesis that skin conditions and depressive disorders are concurrently associated in older adults. However, no evidence of the predictive effect of skin problems on depression (and vice versa) was found in our community sample. Despite the deleterious effect of the coexistence of these problems in older adults, studies are lacking. This article highlights the importance of this issue and emphasizes the need for further research on this topic.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.037
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.012
GPT teacher head0.268
Teacher spread0.256 · 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

Citations5
Published2014
Admission routes3
Has abstractyes

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