MétaCan
Menu
Back to cohort
Record W2029096807 · doi:10.1348/135910709x432745

A psychometric evaluation of the Hospital Anxiety and Depression Scale in cardiac patients: Addressing factor structure and gender invariance

2009· article· en· W2029096807 on OpenAlexaff
Tiffany T. Hunt-Shanks, Christopher Blanchard, Robert Reid, Michelle Fortier, Mario Cappelli

Bibliographic record

VenueBritish Journal of Health Psychology · 2009
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsChildren's Hospital of Eastern OntarioQueen Elizabeth II Health Sciences CentreMontreal Heart InstituteDalhousie UniversityUniversity of Ottawa
Fundersnot available
KeywordsConfirmatory factor analysisMeasurement invarianceHospital Anxiety and Depression ScaleStructural equation modelingPsychologyAnxietyClinical psychologyDepression (economics)DistressPsychometricsPsychiatryStatistics

Abstract

fetched live from OpenAlex

OBJECTIVES: The present study examined the factor structure of the Hospital Anxiety and Depression Scale (HADS) and tested measurement invariance between genders in a representative sample of cardiac patients across 2 years. DESIGN: Confirmatory factor analysis and structural equational modelling were used to assess the factor structure, measurement, and structural invariance of the HADS. METHODS: Eight hundred and one cardiac patients completed the HADS at baseline, 6, 12, and 24 months. RESULTS: Confirmatory factor analysis consistently supported a three-factor structure of the HADS, with the best fitting model comprised of negative affect, autonomic anxiety, and depression. Structural equation modelling showed that the HADS was invariant by gender among cardiac patients. CONCLUSIONS: The HADS can be appropriately used with both male and female cardiac patients to assess three domains of psychological distress. Future investigations should consider the predictive validity and relevance of the HADS subscales with respect to diagnostic distinctions and clinical outcomes among cardiac patients and other clinical populations.

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 score0.435
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.049
GPT teacher head0.398
Teacher spread0.349 · 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

Citations48
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueBritish Journal of Health PsychologySame topicCardiac Health and Mental HealthFrench-language works237,207