Poster Presentations from the 7th Canadian Conference on Dementia (CCD) held in Vancouver, October 2013
Bibliographic record
Abstract
Objectives: To identify primary care doctors knowledge, practices, and obstacles with regard to the diagnosis and management of dementia.Methods: Standardized questionnaires covering knowledge, practices, and obstacles were distributed among a random sample of primary care doctors in Kathmandu, Nepal.380 physicians responded (response rate = 89%).Results: Knowledge of practitioners with regard to the diagnosis and management of dementia was unsatisfactory.Diagnosis and management barriers are presented with regard to GP factors, patient factors, systemic factors, and carer factors.Discussion: Specifically, the results address the following issues: time, communicating the diagnosis, negative views of dementia, difficulty diagnosing early stage dementia, acceptability of specialists and responsibility for extra issues, knowledge of dementia and ageing, less awareness of declining abilities and diminished resources to handle care, not specified guidelines, poor awareness of epidemiology, and less confidence to advise.Conclusions: Demographic changes mean that dementia will represent a significant problem in the future.The following paper outlines the problems and solutions that the Nepalese Abstracts from the 7 th Canadian Conference on Dementia (CCD)
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.260 | 0.037 |
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".