Polish adaptation of the Dimensional Anhedonia Rating Scale (DARS) - validation in the clinical sample
Bibliographic record
Abstract
Background: Anhedonia is the core symptom of depression. Its presence has been linked to worsened prognosis. The Dimensional Anhedonia Rating Scale (DARS) is a scale measuring desire, motivation, effort and consummatory pleasure across different domains. The aim of this paper was to confirm factor structure, assess reliability and validity of the Polish adaptation of the DARS in a clinical sample of patients with mood disorders and healthy controls (HC). Methods: The study sample included 161 participants aged 18-65 years - 34 HC, 72 patients with bipolar disorder and 55 with major depressive disorder (in depressive episode or remission). Reliability of the Polish adaptation of the DARS was assessed using Cronbach's α and the average inter-item correlation (AIC). Convergent and divergent validity was established by Pearson's correlations between the DARS and the Snaith-Hamilton Pleasure Scale (SHAPS), the Quick Inventory of Depressive Symptomatology- self-report (QIDS-SR), the Hospital Anxiety and Depression Scale (HADS). The structure of the scale was examined by factor analysis. Results: The factor structure was consistent with the original scale. Strong internal consistency for the DARS total score (Cronbach's α = 0.95) and all subscales (0.86-0.93) was observed. The DARS demonstrated good convergent (moderate to strong correlations with measures of anhedonia and depression) and divergent validity (weak correlations with anxiety level). Conclusion: The Polish DARS demonstrated excellent internal consistency and very good validity. The scale is a valuable contribution to the psychometrics of anhedonia measures in patients with mood disorders.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".