P.005 A virtual interdisciplinary diagnostic memory clinic: rural patient and caregiver satisfaction
Bibliographic record
Abstract
Background: Saskatchewan’s Rural and Remote Memory Clinic (RRMC) has provided post-diagnostic virtual dementia care for approximately 19 years. In response to the COVID-19 pandemic and a new need for remote dementia diagnosis, we developed a virtual, team-based, interdisciplinary (neurology, neuropsychology, nursing), diagnostic memory clinic (vRRMC). We evaluated patient and caregiver satisfaction with the new virtual clinic. Methods: Semi-structured telephone interviews were conducted with rural vRRMC patients (n=7), caregivers (n= 13), and one patient/caregiver dyad. Ages of respondents ranged from 40 to 70 years old (60% female). Level of diagnosed cognitive dysfunction ranged from subjective cognitive impairment to major neurocognitive disorder. Respondents saved an average of 460 km of travel compared to a trip to Saskatoon. Results: Thematic analysis of responses revealed universal satisfaction with the virtual model. The technology training sessions, offered prior to the first vRRMC visit, was described as important for satisfaction. Analysis of preference for future visits revealed more nuance; some preferred in-person visits and planned to travel for future appointments post-pandemic, while others preferred to maintain the virtual model due to perceived travel burden (cost, time, and inconvenience). Conclusions: When clinically appropriate, virtual diagnostic memory clinics should persist as an option post pandemic for families who experience high travel burden.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".