Reliability and construct validity of the Long Version of Disability Assessment for Dementia (Brazilian Version)
Bibliographic record
Abstract
Abstract Objectives Evaluate the reliability test-retest (intra and inter-examiner) and construct validity of the Long Version of Disability Assessment for Dementia (Brazilian Version - DADL-BR). Methods The DADL-BR was applied to 58 caregivers/family of older adults with dementia. The inter-examiner (n = 30) and intra-examiner (n = 28) reliability was assessed using the kappa test and Pearson’s correlation coefficient, and the internal consistency was assessed using Cronbach’s alpha. The construct validity (n = 48) was performed by comparing DADL-BR with MMSE. Results The results of the intra and inter-examiner demonstrated good reliability (0.72 and 0.74), as well as nearly perfect correlations (0.99) for both inter- and intra-examiner reliability. The internal consistency was excellent (0.87) and the results demonstrated a good correlation (0.74) between DADL-BR and MMSE in construct validity analysis. Conclusions DADL-BR can be considered reliable instrument and with good construct validity, considering internal consistency and test-retest reliability and may be constitute a useful instrument for assessing the occupational performance profile of older adults with dementia.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".