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Record W2015949147 · doi:10.9788/tp2014.1-19

Development and psychometric properties of a novel depression measure

2014· article· en· W2015949147 on OpenAlexaff
Alberto Filgueiras, Gabriela Hora, Ana Carolina Monneratt Fioravanti-Bastos, Cristina M. T. Santana, Pedro Pires, Bruno de Oliveira Galvão, J. Landeira-Fernández

Bibliographic record

VenueTemas em Psicologia · 2014
Typearticle
Languageen
FieldPsychology
TopicSocial Representations and Identity
Canadian institutionsWestern University
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoMinistério da EducaçãoCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorAustralian Government
KeywordsMeasure (data warehouse)Depression (economics)PsychologyPsychometricsClinical psychologyComputer scienceData miningEconomics

Abstract

fetched live from OpenAlex

The Filgueiras Depression Inventory is proposed as a new instrument, created specifi cally for the Brazilian culture, for screening of Major Depressive Episodes according to the categories of the DSM-V. Two studies were conducted for this purpose. Study's 1 sample consisted of 326 undergraduate psychology students. Single words or expressions were asked to represent overall depressive symptoms. The most cited formed the fi rst version of the Filgueiras Depression Inventory. Study 2 reported the psychometric properties of this new scale. The sample consisted of 471 volunteers recruited on the Internet and 238 volunteers undergraduate students. Factor analyses, convergent and discriminant validity and other Classical Test Theory indices revealed results consistent to expectations in the present study. Andrich's Rating Scale Modeling was used as an Item Response Theory method of analysis. The overall psychometric properties of the Filgueiras Depression Inventory were shown to be good, and this study supports the effectiveness of this scale as a new instrument that measures depressive episodes in the Brazil.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.103
GPT teacher head0.336
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2014
Admission routes1
Has abstractyes

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