The Psychometric Characteristics of the Hamilton Depression Inventory
Bibliographic record
Abstract
In this study, the psychometric properties of the Hamilton Depression Inventory (HDI; Reynolds & Kobak, 1995a) were examined in a sample of 249 undergraduate participants. The HDI exhibited high internal consistency and support for its construct validity was demonstrated by the HDI's patterns of correlations with other measures of depression, anxiety, and depression-relevant cognition. Factor analyses of the full (23-item) and 17-item versions of the HDI each yielded 4 factors, which accounted for 49% and 53% of the variance in participants' responses, respectively. The utility of the HDI's use of multiple-weighted subitems was also assessed by comparing a less complicated scoring system to the standard scoring format. The standard HDI added significantly to the prediction of criterion indexes after controlling for the variance accounted for by the "simplified" HDI. Moreover, the operating characteristics of the standard HDI outperformed the simplified HDI in the prediction of the Beck Depression Inventory-II (Beck, Steer, & Brown, 1996) classification. The results provide strong support for the HDI as a reliable and valid instrument for the assessment of depressive severity
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".