Development and Initial Validation of a Brief, Online Version of the Center for Epidemiological Studies Depression Scale (CES-D): Psychometric Study
Bibliographic record
Abstract
BACKGROUND: A growing volume of mental health research is conducted with participants recruited and responding online. However, to date, few psychometric scales have been specifically validated for online research. OBJECTIVE: We aimed to devise a brief, 12-item version of the Center for Epidemiological Studies Depression Scale (CES-D) in which first order factors are sufficiently measured. METHODS: We recruited 218 adults with depression and 226 comparison participants with no mental health history. Both groups completed the original 20-item CES-D and measures of social support, psychological distress, and sociodemographic information (eg, age, gender, and household income). Measurement of social support included online support, and psychological distress included symptoms of social media use disorder along with loneliness and life dissatisfaction. RESULTS: This brief, 12-item version of the CES-D was devised with persons with depression and replicated with comparison participants. For both, core sadness, somatic symptoms, interpersonal detachment, and absence of well-being each significantly contributed to measurement of a higher-order depression latent construct (P<.01). Structural equation modeling was performed to establish the construct validity of this 4-factor model in which depression is predicted by socioeconomic factors and depression predicts lower social support as well as greater psychological distress. CONCLUSIONS: Responses to this 12-item, online version of the CES-D demonstrate factorial and construct validity. Clinical research is required in future to ascertain whether scores greater than 11 (of 36) are suggestive of elevated depressive symptomology.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".