Attitudes of the German General Population toward Early Diagnosis of Dementia – Results of a Representative Telephone Survey
Bibliographic record
Abstract
BACKGROUND: Early detection of dementia has clearly improved. Even though none of the currently available treatments for the most common form of dementia, Alzheimer's dementia, promises a cure, early diagnosis provides several benefits for patients, caregivers, and health care systems. This study aimed to describe attitudes toward early diagnosis of dementia in the German general population. METHODS: A representative telephone survey of the German population aged 18+ years (n = 1,002) was conducted in 2011. RESULTS: The majority of respondents (69%) would be willing to be examined for early diagnosis of dementia. Almost two thirds reported that they would prefer their general practitioner (GP) as the first source of professional help. More than half of the respondents (55%) stated their belief that dementia could be prevented. Respondents mostly indicated psychosocial prevention options. CONCLUSIONS: Our findings suggest that the general population in Germany is very open to early diagnosis of dementia; however, this seems connected with large expectations on the effectiveness of prevention options. Dementia awareness campaigns may be employed to carefully inform the public about the prevention options currently available and their efficacy. To exploit GPs' potential as a gatekeeper for early detection of dementia, their ability to identify patients with antecedent and mild stages of the disease must be improved.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".