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Record W2094846879 · doi:10.1007/s12160-013-9534-2

Longitudinal Relationships Between Depression and Functioning in People with Type 2 Diabetes

2013· article· en· W2094846879 on OpenAlexafffund
Norbert Schmitz, Geneviève Gariépy, Kimberley J. Smith, Ashok Malla, Richard Boyer, Irène Strychar, Alain Lesage, JianLi Wang

Bibliographic record

VenueAnnals of Behavioral Medicine · 2013
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsHôpital Louis-H LafontaineUniversité de MontréalUniversity of CalgaryCentre Hospitalier de l’Université de MontréalMcGill UniversityDouglas Mental Health University Institute
FundersCanadian Institutes of Health Research
KeywordsDepression (economics)Type 2 diabetesHealth psychologyPsychologyClinical psychologyPsychiatryMedicineDiabetes mellitusGerontologyPublic healthEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: The reciprocal relationship between depression and functioning in people with chronic conditions is poorly understood. PURPOSE: The aim of the present study was to analyze the dynamic relationship between depression and functioning in a community sample of people with diabetes. METHODS: Participants with diabetes were assessed at baseline and three yearly follow-up assessments (n = 1,403). Depression was assessed using the Patient Health Questionnaire. Global functioning was assessed using the World Health Organization Disability Assessment Schedule II. RESULTS: Path analysis suggested a reciprocal relationship between depression and functioning. Baseline depression was associated with functioning at 3 years follow-up through depression and functioning at 1 and 2 years follow-up assessments. CONCLUSIONS: Depression and functioning might interact with each other in a dynamic way: depression at one assessment point might predict poor functioning at the next assessment point, which in turn might predict depression at the next assessment point. This should be taken into account in both treatment and research programs.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.014
Threshold uncertainty score0.277

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.128
GPT teacher head0.355
Teacher spread0.226 · 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

Citations13
Published2013
Admission routes2
Has abstractyes

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