COSTS OF A STAFF COMMUNICATION INTERVENTION TO REDUCE DEMENTIA BEHAVIORS IN NURSING HOME CARE
Bibliographic record
Abstract
CONTEXT: Persons with Alzheimer's disease and other dementias experience behavioral symptoms that frequently result in nursing home (NH) placement. Managing behavioral symptoms in the NH increases staff time required to complete care, and adds to staff stress and turnover, with estimated cost increases of 30%. The Changing Talk to Reduce Resistivenes to Dementia Care (CHAT) study found that an intervention that improved staff communication by reducing elderspeak led to reduced behavioral symptoms of dementia or resistiveness to care (RTC). OBJECTIVE: This analysis evaluates the cost-effectiveness of the CHAT intervention to reduce elderspeak communication by staff and RTC behaviors of NH residents with dementia. DESIGN: Costs to provide the intervention were determined in eleven NHs that participated in the CHAT study during 2011-2013 using process-based costing. Each NH provided data on staff wages for the quarter before and for two quarters after the CHAT intervention. An incremental cost-effectiveness analysis was completed. ANALYSIS: An average cost per participant was calculated based on the number and type of staff attending the CHAT training, plus materials and interventionist time. Regression estimates from the parent study then were applied to determine costs per unit reduction in staff elderspeak communication and resident RTC. RESULTS: A one percentage point reduction in elderspeak costs $6.75 per staff member with average baseline elderspeak usage. Assuming that each staff cares for 2 residents with RTC, a one percentage point reduction in RTC costs $4.31 per resident using average baseline RTC. CONCLUSIONS: Costs to reduce elderspeak and RTC depend on baseline levels of elderspeak and RTC, as well as the number of staff participating in CHAT training and numbers of residents with dementia-related behaviors. Overall, the 3-session CHAT training program is a cost-effective intervention for reducing RTC behaviors in dementia care.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".