EVALUATION OF A NURSE PRACTITIONER-LED VIRTUAL BEHAVIORAL MEDICINE PROGRAM: INNOVATIVE NEUROPSYCHIATRIC CARE
Bibliographic record
Abstract
Abstract People living with dementia in long-term care (LTC) who exhibit severe neuropsychiatric symptoms are often at risk of being transferred to the hospital from their familiar environment. The virtual Behavioral Medicine (VBM) model was designed to manage these symptoms with intensive virtual support within LTC to prevent hospitalization. We adapted this model to a nurse practitioner (NP)-led approach and conducted a program evaluation to assess its implementation and preliminary effectiveness during its first year. Between April 2023 to March 2024, 102 patients were referred for management of severe neuropsychiatric symptoms and/or consideration for admission to our psychogeriatric unit. The average wait time from referral to the first consultation was 10 days, with follow-ups occurring every 7.6 days by an NP and every 16.2 days involving a geriatric psychiatrist. The average length of stay in the program was 41 days. The average Neuropsychiatric Inventory score improved from 31 to 9.4 at discharge, and the number of medications decreased from 16 to 9. Among the 102 patients, 48 (47%) improved with VBM alone, avoiding hospital admission. In a survey of 60 LTC teams, 49 (81%) responded. Over 90% noted improvement in neuropsychiatric symptoms and expressed satisfaction with the quality of care and support provided by the VBM team. All respondents felt heard and more equipped to care for residents in-house, though some reported challenges with the virtual technology. These first-year insights into operations will inform continuous quality improvement. Further evaluation of caregiver satisfaction and the effectiveness of the NP-led model is warranted.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".