Implementing a Personalized Integrated Stepped-Care Method (STIP-Method) to Prevent and Treat Neuropsychiatric Symptoms in Persons With Dementia in Nursing Homes: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Neuropsychiatric symptoms occur frequently in many nursing home residents with dementia. Despite the availability of multidisciplinary guidelines, neuropsychiatric symptoms are often inadequately managed. Three proven effective methods for managing neuropsychiatric symptoms were integrated into a single intervention method: the STIP-Method, a personalized integrated stepped-care method to prevent and treat neuropsychiatric symptoms. The STIP-Method comprises 5 phases of clinical reasoning to neuropsychiatric symptoms and 4 stepped-care interventions and is supported with a web application. OBJECTIVE: This study aims to identify the facilitators and barriers in the implementation of the STIP-Method in nursing homes. METHODS: A mixed methods design within a participatory action research was used to implement the STIP-Method in 4 facilities of 2 Dutch nursing home organizations. In total, we aimed at participation of 160-200 persons with dementia and expected an intervention fidelity of 50% or more, based on earlier studies regarding implementation of effective psychosocial interventions to manage neuropsychiatric symptoms. All involved managers and professionals were trained in the principles of the STIP-Method and in using the web application. An advisory board of professionals, managers, and informal caregivers in each facility supported the implementation during 21 months, including an intermission of 6 months due to the COVID-19 pandemic. In these 6-weekly advisory board meetings, 2 researchers stimulated the members to reflect on progress of the implementation by making use of available data from patient records and the web application. Additionally, the 2 researchers invited the members to suggest how to improve the implementation. Data analysis will involve (1) analysis of facilitators and barriers to the implementation derived from verbatim text reports of advisory board meetings to better understand the implementation process; (2) analysis of patient records in accordance with multidisciplinary guidelines to neuropsychiatric symptoms: personalized, interdisciplinary, and proactive management of neuropsychiatric symptoms; (3) evaluation of the web application in terms of usability scores; (4) pre- and postimplementation analysis of patient records and the web application to evaluate the impact of the STIP-Method, such as changes in neuropsychiatric symptoms and informal caregiver burden. RESULTS: We enrolled 328 persons with dementia. Data collection started in July 2019 and ended in December 2021. The first version of this manuscript was submitted in October 2021. The first results of data analysis are expected to be published in December 2022 and final results in June 2023. CONCLUSIONS: Our study may increase understanding of facilitators and barriers to the prevention and treatment of neuropsychiatric symptoms in nursing home residents with dementia by implementing the integrated STIP-Method. The need for well-designed implementation studies is of importance to provide nursing homes with optimal tools to prevent and treat neuropsychiatric symptoms. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34550.
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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 | 0.052 | 0.036 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.041 | 0.007 |
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".