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Record W2161016026 · doi:10.3109/01612840903484105

Factorial Validity of the Center for Epidemiological Studies Depression 10 in Adolescents

2010· article· en· W2161016026 on OpenAlexaff
Kristina L. Bradley, Alexa Bagnell, Cyndi Brannen

Bibliographic record

VenueIssues in Mental Health Nursing · 2010
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsDalhousie University
Fundersnot available
KeywordsStructural equation modelingPsychologyCenter for Epidemiologic Studies Depression ScaleClinical psychologyAffect (linguistics)Depression (economics)EpidemiologyPopulationPsychometricsScale (ratio)Test validityPsychiatryMedicineDepressive symptomsStatisticsEnvironmental healthAnxiety

Abstract

fetched live from OpenAlex

The Center for Epidemiological Studies-Depression (CES-D) Scale's 20-item version is well-validated and reliable for detecting depressive symptoms in adolescents in community samples. A shortened version, CES-D 10 has not been validated with adolescents, but has demonstrated strong psychometrics in other populations. The purpose of this study was to test the factorial validity and internal consistency of the CES-D 10 in adolescents. Using data from 156 adolescents in a previous community-based study, we tested three models of the underlying factors of the CES-D 10 using Structural Equation Modeling (SEM) based on factor models validated in other populations. A two-factor model comprised of depressive affect and positive affect was found to be the model that best fits the data (RMSEA = 0.016, CFI = 0.98, GFI = 0.95, AIC = 97.43, BIC = 191.98). These findings are consistent with other studies in adults and provide initial support for the use of the CES-D 10 as a depression screen for adolescents in the community. The utility of a brief screen for adolescents in the community is high, given that many adolescents do not know they need help or are reluctant to seek help. The CES-D 10 could be used as a depression screen for adolescents at a population level and in health clinics.

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)0.000
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 score0.053
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.104
GPT teacher head0.465
Teacher spread0.361 · 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

Citations256
Published2010
Admission routes1
Has abstractyes

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