Evaluation of the Internal Consistency, Factor Structure, and Validity of the Depression Change Expectancy Scale
Bibliographic record
Abstract
The psychometric properties and predictive validity of the Depression Change Expectancy Scale (DCES), a modification of an expectancy scale originally developed for patients with anxiety disorders, were examined in two studies. In Study 1, the 20-item scale was administered along with a battery of questionnaires to a sample of 416 dysphoric undergraduate students and demonstrated good internal consistency. A two-factor solution most parsimoniously accounted for the variance, with one factor containing all pessimistically worded items (DCES-P) and the second containing all optimistically worded items (DCES-O). The DCES-P showed patterns of correlations with other measures of related constructs consistent with hypothesized relationships; the DCES-O showed similar, but weaker, relationships with the other measures. Multilevel modeling was used to examine the predictive utility of the DCES in a clinical sample of 63 adults (Study 2). Improved depressive symptoms (over 6 weeks) were strongly associated with optimistic expectancies but were unrelated to pessimistic expectancies for change. The DCES appears to be a promising measure of expectancies for improvement among individuals with depressive symptoms.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 0.000 |
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.
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".