Navigating Dementia Care: An Integrated Program for People with Dementia, Care Partners, and the Care Team
Bibliographic record
Abstract
Dementia care is often fragmented and difficult to navigate. Patient navigation (PN) is one solution to address the care needs of people with dementia (PWD) and their care partners. Navigating Dementia NB/ Naviguer la démence NB was a research project that piloted a patient navigation (PN) program in a small semi-rural province in Canada for PWD, their care partners, and the care team. The intervention was co-designed with multiple stakeholders, including clinicians, researchers, patient partners, and representatives from Regional Health Authorities. For example, the research team collaborated with representatives from the Regional Health Authorities (e.g., directors, clinic managers) to select the most appropriate clinical sites for this intervention and to manage day-to-day operations of the intervention. A Patient and Family Advisory Committee (PFAC) assisted with program oversight and a member of PFAC also sat on the project organizational committee. The PN program aimed to guide and support patients and their families through health and social care systems, matching client needs to appropriate services/resources. Six patient navigators (4 anglophone and 2 francophone) were embedded in preexisting primary care clinics/health centres in urban and rural settings across the province. The role of the patient navigator was to increase participants’ knowledge of health and social services/resources related to dementia care, to improve access to these services/resources through in person and online patient navigation, and to improve communication pathways that promote the integration and coordination of care. A mixed methods approach was used to evaluate the program, which was piloted for 12 months (July 2022-July 2023). Data for this evaluation was collected from patient navigator charts, satisfaction surveys, and semi-structured interviews with participants and stakeholders involved in creating and implementing the program. Across sites, 150 participants took part in the study. Reasons for contacting the navigators included: connecting with social services, dementia specific information and resources, advance care planning, community resources, and home health care. Fifty-six participants returned post-intervention satisfaction surveys. The survey data indicated that 85% of participants were generally satisfied with services from the program. Seventy-eight percent of participants reported having greater knowledge of health and/or social services and resources because of the patient navigator and 76% of participants reported having greater access to health and/or social services and resources. Thirty-seven participants completed post-intervention interviews about their experiences with the program, and qualitative content analysis of this data is underway. Preliminary analysis identified the following themes: overall satisfaction with the program, supportive tasks, systemic barriers, and recommendations for program improvement. These results suggest that PN, embedded in existing primary care clinics/health centers, is beneficial for PWD and their care partners. Furthermore, patient navigation is a flexible model of care and can be easily adapted to different populations and regions. These findings support our aim to promote positive experiences with health and social care systems for this population and promote person-centred, integrated care.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".