Factorial Validity and Gender Invariance of the Center for Epidemiological Studies Depression in Cardiac Rehabilitation Patients
Bibliographic record
Abstract
In Brief PURPOSE: To find a best-fitting factor structure of the Center for Epidemiological Studies Depression (CES-D) and test whether this structure is invariant across gender in a cardiac rehabilitation population. METHODS: We examined the data from 920 participants of a cardiac rehabilitation exercise program. Fourteen confirmatory factor analyses were conducted to examine existing factor solutions from the literature. The best-fitting model was tested for invariance across gender. RESULTS: The data fit best to a 3-factor solution, which has 14 items and 3 factors (ie, somatic symptoms, negative affect, and anhedonia). The goodness-of-fit indices showed an acceptable fit. The invariance analysis revealed that the factor structure is equivalent across gender. CONCLUSIONS: While a fitting factor solution was found, rehabilitation practitioners and researchers need to be aware of the psychometrical shortcomings of the CES-D and consider using other scales as alternative measures of depressive symptoms. This study examined an appropriate factor structure for the CES-D and tested for gender invariance in cardiac rehabilitation participants. Fourteen confirmatory factor analyses revealed a best-fitting 3-factor, 14-item structure. Gender invariance was confirmed for the model. However, practitioners and researchers need to be aware of the psychometrical shortcomings of the scale.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".