Developing a Plan for the Sustainable Implementation of an Electronic Health Intervention (Partner in Balance) to Support Caregivers of People With Dementia: Case Study
Bibliographic record
Abstract
BACKGROUND: Given the increasing use of digital interventions in health care, understanding how best to implement them is crucial. However, evidence on how to implement new academically developed interventions in complex health care environments is lacking. This case study offers an example of how to develop a theory-based implementation plan for Partner in Balance, an electronic health (eHealth) intervention to support the caregivers of people with dementia. OBJECTIVE: The specific objectives of this study were to (1) formulate evidence-based implementation strategies, (2) develop a sustainable business model, and (3) integrate these elements into an implementation plan. METHODS: This case study concerns Partner in Balance, a blended care intervention to support the caregivers of people with dementia, which is effective in improving caregiver self-efficacy, quality of life, and experienced control. The large-scale implementation of Partner in Balance took place in local dementia case-management services, local care homes, dementia support groups, and municipalities. Experiences from real-life pilots (n=22) and qualitative interviews with national stakeholders (n=14) were used to establish an implementation plan consisting of implementation strategies and a business model. RESULTS: The main finding was the need for a business model to facilitate decision-making from potential client organizations, who need reliable pricing information before they can commit to training coaches and implementing the intervention. Additionally, knowledge of the organizational context and a wider health care system are essential to ensure that the intervention meets the needs of its target users. Based on these findings, the research team formulated implementation strategies targeted at the engagement of organizations and staff, dissemination of the intervention, and facilitation of long-term project management in the future. CONCLUSIONS: This study offers a theory-based example of implementing an evidence-based eHealth intervention in dementia health care. The findings help fill the knowledge gap on the eHealth implementation context for evidence-based eHealth interventions after the trial phase, and they can be used to inform individuals working to develop and sustainably implement eHealth.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".