How much do library students know about dementia? Findings from a quantitative study using the Alzheimer’s Disease Knowledge Scale
Bibliographic record
Abstract
The goal of the study presented in the paper is to assess the knowledge about Alzheimer’s Disease (AD), among library and information science students in Croatia. Understanding how much future librarians know about dementia is the first step towards providing them with relevant educational intervention which will equip them with required knowledge to develop dementia-friendly library services in a society which is increasingly affected by dementia. A total of 183 students participated in the study which used Alzheimer’s Disease Knowledge Scale (ADKS), a validated instrument that measures what people know about AD using a 30-item questionnaire across seven knowledge domains: risk factors, symptoms, assessment and diagnosis, course of the disease, life impact, treatment, and management, and caregiving. The collected data were analysed using basic descriptive statistics and a parametric test ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="t" display="inline" overflow="scroll"> <mml:mi>t</mml:mi> </mml:math> -test). Findings show that respondents have poor AD knowledge. Only 35.78% questions were answered correctly and the mean knowledge score was 10.76. The findings revealed that participants with previous exposure to the disease have significantly better knowledge ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="P=" display="inline" overflow="scroll"> <mml:mrow> <mml:mi>P</mml:mi> <mml:mo>=</mml:mo> <mml:mi/> </mml:mrow> </mml:math> 0.003).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".