Virtual Behavioural Medicine Program: A Novel Model of Care for Neuropsychiatric Symptoms in Dementia1
Bibliographic record
Abstract
BACKGROUND: Patients with severe neuropsychiatric symptoms (NPS) due to dementia are often uprooted from their familiar environments in long-term care or the community and transferred to emergency departments, acute care hospitals, or specialized behavioral units which can exacerbate NPS. To address this issue, we developed the Virtual Behavioural Medicine Program (VBM), an innovative model of virtual care designed to support management of patients with NPS in their own environment. OBJECTIVE: To determine efficacy of VBM in reducing admission to a specialized inpatient neurobehavioral unit for management of NPS. METHODS: We reviewed outcomes in the first consecutive 95 patients referred to VBM. Referrals were classified into two groups. In one group, patients were referred to VBM with a simultaneous application to an inpatient Behavioural Neurology Unit (BNU). The other group was referred only to VBM. The primary outcome was reduction in proportion of patients requiring admission to the BNU regardless of whether they were referred to the BNU or to VBM alone. RESULTS: For patients referred to VBM plus the BNU, the proportion needing admission to the BNU was reduced by 60.42%. For patients referred to VBM alone, it was 68.75%. CONCLUSION: VBM is a novel virtual neurobehavioral unit for treatment of NPS. Although the sample size was relatively small, especially for the VBM group, the data suggest that this program is a game changer that can reduce preventable emergency department visits and acute care hospital admissions. VBM is a scalable model of virtual care that can be adopted worldwide.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".