Quantifying Knowledge of Alzheimer’s Disease: An Analysis of the Psychometric Properties of the Alzheimer’s Disease Knowledge Scale
Bibliographic record
Abstract
INTRODUCTION: The Alzheimer's Disease Knowledge Scale (ADKS) is one of the most popular instruments for assessing a person's knowledge regarding Alzheimer's disease (AD). The objective of this study was to explore ADKS item characteristics with item response theory (IRT) procedures. METHODS: A noninterventional web-based study was conducted. A nonparametric IRT procedure, Mokken analysis, was used to explore the underlying latent structure of the ADKS and ADKS item characteristics regarding scalability and violations of the monotone homogeneity (MH) model. A random-effects meta-analysis was implemented that combined ADKS scores from independent studies. RESULTS: A total of 447 employees of a pharmaceutical company participated in the study. The mean ADKS score was 21.2 (SD 2.8). Mokken analysis showed that most ADKS items (22 of 30) do not fit to any scale and can be considered to be scale independent. Two items (#1: particularly prone to depression; #20: depression can be mistaken for AD) fit to a domain relating to depression, another two items (#2: mental exercise can prevent AD development; #8: benefit of psychotherapy) can be related to potential prevention and improvement, and four items (#12: poor nutrition can make the symptoms worse; #18: high cholesterol may increase the risk of AD; #26: high blood pressure may increase the risk of AD; #27: genes can only partially account for AD development) fit to a risk factor domain. As expected from those results, neither the overall scale (H = 0.033) nor its items showed appropriate scalability index values, suggesting that ADKS does not fit to a MH model. Eleven items showed violations of the assumptions of the MH model. The meta-analytical average score was 21.78 (95% CI 20.67-22.90), with healthcare professionals and caregivers showing the highest levels of AD knowledge. CONCLUSION: Although the ADKS does not present a unidimensional structure, its independent items together provide a comprehensive spectrum of information regarding AD knowledge.
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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 | 0.052 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".