Discrepancies in Cornell Scale for Depression in Dementia (CSDD) items between residents and caregivers, and the CSDD's factor structure
Bibliographic record
Abstract
PURPOSE: This validation study aims to examine Cornell Scale for Depression in Dementia (CSDD) items in terms of the agreement found between residents and caregivers, and also to compare alternative models of the Thai version of the CSDD. PATIENTS AND METHODS: A cross-sectional study was conducted of 84 elderly residents (46 women, 38 men, age range 60-94 years) in a long-term residential home setting in Thailand between March and June 2011. The selected residents went through a comprehensive geriatric assessment that included use of the Mini-Mental State Examination, Mini-International Neuropsychiatric Interview, and CSDD instruments. Intraclass correlation (ICC) was calculated in order to establish the level of agreement between the residents and caregivers, in light of the residents' cognitive status. Confirmatory factor analysis (CFA) was adopted to evaluate the alternative CSDD models. RESULTS: The CSDD yielded a high internal consistency (Cronbach's alpha = 0.87) and moderate agreement between residents and caregivers (ICC = 0.55); however, it was stronger in cognitively impaired subjects (ICC = 0.71). CFA revealed that there was no difference between the four-factor model, in which factors A (mood-related signs) and E (ideational disturbance) were collapsed into a single factor, and the five-factor model as per the original theoretical construct. Both models were found to be similar, and displayed a poor fit. CONCLUSION: The CSDD demonstrated a moderate level of interrater agreement between residents and caregivers, and was more reliable when used with cognitively impaired residents. CFA indicated a poorly fitting model in this sample.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".