A Review of Barriers and Enablers to Diagnosis and Management of Persons with Dementia in Primary Care
Bibliographic record
Abstract
BACKGROUND: With the rise in the prevalence of dementia disorders and the growing critical impact of dementia on health-care resources, the provision of dementia care has increasingly come under scrutiny, with primary care physicians (PCP) being at the centre of such attention. PURPOSE: To critically examine barriers and enablers to timely diagnosis and optimal management of community living persons with dementia (PWD) in primary care. METHODS: An interpretive scoping review was used to synthesize and analyze an extensive body of heterogeneous Western literature published over the past decade. RESULTS: The current primary care systems in many Western countries, including Canada, face many challenges in providing responsive, comprehensive, safe, and cost-effective dementia care. This paper has identified a multitude of highly inter-related obstacles to optimal primary dementia care, including challenges related to: a) the complex biomedical, psychosocial, and ethical nature of the condition; b) the gaps in knowledge, skills, attitudes, and resources of PWD/caregivers and their primary care providers; and c) the broader systemic and structural barriers negatively affecting the context of dementia care. CONCLUSIONS: Further progress will require a coordinated campaign and significantly increased levels of commitment and effort, which should be ideally orchestrated by national dementia strategies focusing on the barriers and enablers identified in this paper.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".