Psychometric Evaluation of the Korean Version of the Personhood in Dementia Questionnaire Using Rasch Analysis
Bibliographic record
Abstract
There is an increasing awareness of the need to promote behaviors consistent with the understanding that individuals with dementia deserve adequate respect. Person-centered attitudes on the part of a care facility's staff can affect care practices and relationships with residents. This study examined the psychometric properties of the Korean version of the Personhood in Dementia Questionnaire (KPDQ), which measures staff's person-centered attitudes toward individuals with dementia. The KPDQ was translated and adapted based on commonly used guidelines from the World Health Organization. For psychometric testing, the data obtained from a total of 269 participants in 13 long-term care facilities were analyzed. Factor analysis, item fit, convergent validity, and known-group validity were examined. Reliability and differential item functioning (DIF) based on Rasch analysis were also assessed. The KPDQ consists of 20 items with three subscales ("agency", "respect for personhood" and "psychosocial engagement"). Item fit statistics indicated that each item fits well with the underlying construct. The KPDQ demonstrated satisfactory convergent validity, known-group validity and internal consistency reliability. There was no DIF by subgroup according to age or educational status. Results indicated that the KPDQ is a reliable and valid tool for measuring long-term care staff's beliefs about personhood.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".