<p>Assessing Knowledge and Perceptions of Alzheimer’s Disease Among Employees of a Pharmaceutical Company in Spain: A Comparison Between Caregivers and Non-Caregivers</p>
Bibliographic record
Abstract
BACKGROUND: Raising knowledge about Alzheimer's disease (AD) may help in identifying the disorder, seeking earlier appropriate healthcare, and decreasing its stigma. The aim of this study was to determine the knowledge and perceptions towards people with AD among employees of a pharmaceutical company in Spain. METHODS: A non-interventional, cross-sectional study was conducted among 447 employees. Participants answered demographic questions and completed the Alzheimer's Disease Knowledge Scale (ADKS). Caregivers also answered questions related to their personal experience with patients with AD and completed the Satisfaction with Life Scale (SWLS), the Revised Memory and Behavior Problems Checklist (RMBPC), and the Beck Depression Inventory-Fast Screen (BDI-FS). RESULTS: Participants were mostly between 30 and 50 years old (63%), female (65.3%), and had bachelor or master degrees (82.7%). Forty-two (9.4%) of participants were caregivers, mainly of moderate to severe dementia subjects. Overall knowledge about AD was moderate (mean ADKS score = 21.2 ± 2.8 [70.6% of correct answers]). Risk factors and caregiving were the domains with lowest scores (correct answers: 58.58% and 63%, respectively). Mean total ADKS score was significantly higher in participants caring for people with AD compared with non-caregivers (22.1 ± 2.9 and 21.0 ± 2.8; p=0.02, respectively). There was no statistically significant association between total ADKS score and age, sex, educational level, or relative's AD severity. Most caregivers were satisfied with life (mean SWLS score = 26.8 ± 5.6) showing a low impact from behavioral problems (mean RMBPC reaction score = 26.81 ± 20.2). Six of them (14.3%) were scored as depressed. CONCLUSION: There is a continuing need to improve understanding of AD to fill the gaps in knowledge of the disease, even in a population working in healthcare sector with a high educational level.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".