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Record W2119475532 · doi:10.1186/1471-244x-13-2

Psychometric properties of responses by clinicians and older adults to a 6-item Hebrew version of the Hamilton Depression Rating Scale (HAM-D6)

2013· article· en· W2119475532 on OpenAlexaff
Yaacov G. Bachner, Norm O’Rourke, Margalit Goldfracht, Per Bech, Liat Ayalon

Bibliographic record

VenueBMC Psychiatry · 2013
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsRating scaleDepression (economics)HebrewPsychologyClinical psychologyPsychometricsPsychiatryScale (ratio)Hamilton Rating Scale for DepressionMajor depressive disorderDevelopmental psychologyMoodHistory

Abstract

fetched live from OpenAlex

BACKGROUND: The Hamilton Depression Rating Scale (HAM-D) is commonly used as a screening instrument, as a continuous measure of change in depressive symptoms over time, and as a means to compare the relative efficacy of treatments. Among several abridged versions, the 6-item HAM-D6 is used most widely in large degree because of its good psychometric properties. The current study compares both self-report and clinician-rated versions of the Hebrew version of this scale. METHODS: A total of 153 Israelis 75 years of age on average participated in this study. The HAM-D(6) was examined using confirmatory factor analytic (CFA) models separately for both patient and clinician responses. RESULTS: Responses to the HAM-D(6) suggest that this instrument measures a unidimensional construct with each of the scales' six items contributing significantly to the measurement. Comparisons between self-report and clinician versions indicate that responses do not significantly differ for 4 of the 6 items. Moreover, 100% sensitivity (and 91% specificity) was found between patient HAM-D6 responses and clinician diagnoses of depression. CONCLUSION: These results indicate that the Hebrew HAM-D(6) can be used to measure and screen for depressive symptoms among elderly patients.

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 score0.029
Threshold uncertainty score0.471

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)0.0000.000

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 head0.013
GPT teacher head0.259
Teacher spread0.246 · 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

Citations21
Published2013
Admission routes1
Has abstractyes

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