Dementia Risk Reduction in Primary Care: A Scoping Review of Clinical Guidelines Using a Behavioral Specificity Framework
Bibliographic record
Abstract
BACKGROUND: Primary care practitioners are being called upon to work with their patients to reduce dementia risk. However, it is unclear who should do what with whom, when, and under what circumstances. OBJECTIVE: This scoping review aimed to identify clinical guidelines for dementia risk reduction (DRR) in primary care settings, synthesize the guidelines into actionable behaviors, and appraise the guidelines for specificity. METHODS: Terms related to "dementia", "guidelines", and "risk reduction" were entered into two academic databases and two web search engines. Guidelines were included if they referred specifically to clinical practices for healthcare professionals for primary prevention of dementia. Included guidelines were analyzed using a directed content analysis method, underpinned by the Action-Actor-Context-Target-Time framework for specifying behavior. RESULTS: Eighteen guidelines were included in the analysis. Together, the guidelines recommended six distinct clusters of actions for DRR. These were to 1) invite patients to discuss DRR, 2) identify patients with risk factors for dementia, 3) discuss DRR, 4) manage dementia risk factors, 5) signpost to additional support, and 6) follow up. Guidelines recommended various actors, contexts, targets, and times for performing these actions. Together, guidelines lacked specificity and were at times contradictory. CONCLUSION: Currently available guidelines allow various approaches to promoting DRR in primary care. Primary care teams are advised to draw on the results of the review to decide which actions to undertake and the locally appropriate actors, contexts, targets, and times for these actions. Documenting these decisions in more specific, local guidelines for promoting DRR should facilitate implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.065 | 0.212 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.033 | 0.033 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".