The Hamilton Depression Rating Scale: Has the Gold Standard Become a Lead Weight?
Bibliographic record
Abstract
OBJECTIVE: The Hamilton Depression Rating Scale has been the gold standard for the assessment of depression for more than 40 years. Criticism of the instrument has been increasing. The authors review studies published since the last major review of this instrument in 1979 that explicitly examine the psychometric properties of the Hamilton depression scale. The authors' goal is to determine whether continued use of the Hamilton depression scale as a measure of treatment outcome is justified. METHOD: MEDLINE was searched for studies published since 1979 that examine psychometric properties of the Hamilton depression scale. Seventy studies were identified and selected, and then grouped into three categories on the basis of the major psychometric properties examined-reliability, item-response characteristics, and validity. RESULTS: The Hamilton depression scale's internal reliability is adequate, but many scale items are poor contributors to the measurement of depression severity; others have poor interrater and retest reliability. For many items, the format for response options is not optimal. Content validity is poor; convergent validity and discriminant validity are adequate. The factor structure of the Hamilton depression scale is multidimensional but with poor replication across samples. CONCLUSIONS: Evidence suggests that the Hamilton depression scale is psychometrically and conceptually flawed. The breadth and severity of the problems militate against efforts to revise the current instrument. After more than 40 years, it is time to embrace a new gold standard for assessment of depression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".