Understanding implementation characteristics in navigation programs for persons living with dementia and their caregivers: A scoping review
Bibliographic record
Abstract
Introduction Dementia care is often fragmented and un-coordinated. As the number of individuals living with dementia increases worldwide, navigation programs are a way to help counter difficulties with navigating and accessing services by better integrating care for individuals with dementia and their family caregivers. While navigation programs are increasingly being used, it is not clear how to best implement such programs in North America and abroad. Methods Following Arskey and O’Malley's (2006) scoping review methodology and theoretically informed by the Consolidated Framework for Implementation Research, this paper explored existing navigational programs used in dementia care to identify factors to consider when implementing these programs across different settings. Results Twenty-two articles were included in this review. Our findings suggest that there is a high degree of variability with how navigation programs are being delivered and no clearly established or standardized protocol to implement such programs. Barriers and facilitators to implementation were identified as they relate to (1) Complexity (Intervention Characteristics); (2) Patient and Caregiver Needs (Outer Setting); (3) External Policies (Outer Setting); (4) Available Resources (Inner Setting) (5) Communication (Inner Setting); (6) Culture (Inner Setting); (7) Leadership Engagement (Inner Setting); (8) Knowledge and Beliefs ( Characteristics of Individuals); (9) Champions (Process) and (10) Evaluation (Process). Discussion Combined, the findings from this review provide suggestions for implementing navigation in the context of dementia care and suggest several pragmatic considerations (e.g. available resources) when implementing new navigation programs.
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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 | 0.039 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".