Perceptions and outcomes of an embedded Alzheimer Society First Link Coordinator in rural primary health care memory clinics
Bibliographic record
Abstract
BACKGROUND: Primary health care has a central role in dementia detection, diagnosis, and management, especially in low-resource rural areas. Care navigation is a strategy to improve integration and access to care, but little is known about how navigators can collaborate with rural primary care teams to support dementia care. In Saskatchewan, Canada, the RaDAR (Rural Dementia Action Research) team partnered with rural primary health care teams to implement interprofessional memory clinics that included an Alzheimer Society First Link Coordinator (FLC) in a navigator role. Study objectives were to examine FLC and clinic team member perspectives of the impact of FLC involvement, and analysis of Alzheimer Society data comparing outcomes associated with three types of navigator-client contacts. METHODS: This study used a mixed-method design. Individual semi-structured interviews were conducted with FLC (n = 3) and clinic team members (n = 6) involved in five clinics. Data were analyzed using thematic inductive analysis. A longitudinal retrospective analysis was conducted with previously collected Alzheimer Society First Link database records. Memory clinic clients were compared to self- and direct-referred clients in the geographic area of the clinics on time to first contact, duration, and number of contacts. RESULTS: Three key themes were identified in both FLC and team interviews: perceived benefits to patients and families of FLC involvement, benefits to memory clinic team members, and impact of rural location. Whereas other team members assessed the patient, only FLC focused on caregivers, providing emotional and psychological support, connection to services, and symptom management. Face-to-face contact helped FLC establish a relationship with caregivers that facilitated future contacts. Team members were relieved knowing caregiver needs were addressed and learned about dementia subtypes and available services they could recommend to non-clinic clients with dementia. Although challenges of rural location included fewer available services and travel challenges in winter, the FLC role was even more important because it may be the only support available. CONCLUSIONS: FLC and team members identified perceived benefits of an embedded FLC for patients, caregivers, and themselves, many of which were linked to the FLC being in person.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".