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Record W4233209528 · doi:10.1590/1413-82712021260413

DASS-21: assessment of psychological distress through the Bifactor Model and item analysis

2021· article· en· W4233209528 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePsico-USF · 2021
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsDASSPsychologyConfirmatory factor analysisAnxietyDistressOperationalizationPsychological distressClinical psychologyStructural equation modelingRating scaleDepression (economics)Construct (python library)PsychiatryDevelopmental psychologyStatistics

Abstract

fetched live from OpenAlex

Abstract The term distress has been used to refer to a continuous variable operationalized through symptoms of depression, anxiety, and stress. In this study, psychological distress is measured using the Depression, Anxiety, and Stress Scale (DASS-21). Confirmatory Factor Analysis compared the fit of different measurement models for the DASS-21, with the parameters of the items verified through the Andrich rating scale model. A non-clinical sample of 530 participants (mean age=24.35±6.55 years; 71.89% women) responded to the instrument. According to the theoretical hypothesis, the results indicated a better fit for the bifactor model, composed of three specific factors (depression, anxiety, and stress) and a general factor (general psychological distress). The assessment of the item properties allowed for a better understanding of the organization of the continuum represented by the construct psychological distress. It is possible to conclude that the Brazilian version of the DASS-21 is an adequate measure for psychological distress.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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

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 head0.061
GPT teacher head0.414
Teacher spread0.353 · 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