Cost‐consequence analysis of an intervention for the management of neuropsychiatric symptoms in young‐onset dementia: Results from the BEYOND‐II study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the cost-consequences of an intervention for the management of neuropsychiatric symptoms in nursing home residents with young-onset dementia. METHODS: A stepped wedge design was used. The intervention consisted of an educational program and a multidisciplinary care program and was implemented in 13 nursing homes from September 2015 to March 2017. Costs' outcomes included the time investment of the elderly care physician and health care psychologists regarding the management of neuropsychiatric symptoms, residents' psychotropic drug use, nursing staff absenteeism, and costs of the educational program. Composite cost measure contained the sum of costs of staff absenteeism, costs on psychotropic drugs, and costs of the educational program. Costs of time investment were investigated by comparing means. Costs of psychotropic drug use were analyzed with mixed models at resident level and as part of the composite cost measure on unit level. Staff absenteeism was also analyzed at unit level. RESULTS: Compared with care as usual, the mean costs of time invested decreased with €36.79 for the elderly care physician but increased with €46.05 for the health care psychologist in the intervention condition. Mixed model analysis showed no effect of the intervention compared with care as usual on the costs of psychotropic drug use, staff absenteeism, and the composite cost measure. The costs of the educational program were on average €174.13 per resident. CONCLUSION: The intervention did not result in increased costs compared with care as usual. Other aspects, such as the lack of a structured working method, should be taken into account when considering implementation of the intervention.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".