Dementia Care and General Physicians - a Survey on Prevalence, Means, Attitudes and Recommendations
Bibliographic record
Abstract
INTRODUCTION: General physicians (GP) play a key role in providing appropriate care for people with dementia. It is important to understand their workload and opinions regarding areas for improvement. MATERIAL AND METHODS: A group of 1,109 GPs working in Mecklenburg-Western Pomerania, Gemany (1.633 million inhabitants), were identified, contacted and asked to participate in a written survey. The survey addressed five main topics: (a) the GP, (b) the GP's practice, (c) the treatment of dementia, (d) personal views, attitudes and specific competences regarding dementia and (e) the GP's recommendations for improving dementia-related health care. RESULTS: The survey response rate was 31%. In total, the responding GPs estimated that they provided care to 12,587 patients with dementia every quarter year. The GPs also reported their opinions about screening instruments, treatment and recommendations for better care of dementia patients. Only 10% of them do not use screening instruments, one third felt competent in their care for patients with dementia and 54% opt for transfer of patients to a specialist for further neuropsychological testing. CONCLUSIONS: Four conclusions from this study are the following: (a) dementia care is a relevant and prevalent topic for GPs, (b) systematic screening instruments are widely used, but treatment is guided, mostly by clinical experience, (c) attitudes towards caring for people with dementia are positive, and (d) GPs recommend spending a lot more time with patients and caregivers and provision of better support in social participation. A majority of GPs recommend abolishing "Budgetierung", a healthcare budgeting system in the statutory health insurance programmes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".