Perspectives From Municipality Officials on the Adoption, Dissemination, and Implementation of Electronic Health Interventions to Support Caregivers of People With Dementia: Inductive Thematic Analysis
Bibliographic record
Abstract
BACKGROUND: Very few evidence-based electronic health (eHealth) interventions for caregivers of people with dementia are implemented into practice. As part of a cross-border collaboration focusing on dementia and depression in older people, two eHealth interventions for caregivers of people with dementia ("Myinlife" and "Partner in Balance") were adopted by nine municipalities in the Euregion Meuse-Rhine. OBJECTIVE: This study aimed to (1) identify determinants for the implementation of eHealth interventions for caregivers of people with dementia in a municipality context and (2) formulate implementation strategies for these interventions. METHODS: Eight municipality officials were interviewed using open-ended, semistructured interviews about their background, thoughts on the implementation of the intervention, recommended strategies, and thoughts on eHealth in general. One additional municipality discontinued the implementation project and submitted answers to the interview questions via email. The interviews were transcribed and independently analyzed using inductive thematic analysis. RESULTS: The interviews provided information on the perspectives of municipality officials on implementing eHealth for caregivers of people with dementia in their local communities. Key findings from the inductive thematic analysis included the importance of face-to-face interviews in developing tailor-made implementation plans, the need for regular meetings, the enthusiasm of municipality officials to implement these interventions, the need for long-term sustainability planning through collecting data on the required resources and benefits, and the effect of name brand recognition in adoption. CONCLUSIONS: The findings contribute toward filling the previously identified gap in the literature on the implementation context of eHealth interventions for caregivers of people with dementia. Municipality officials' views indicated which implementation determinants they expected would influence the adoption, dissemination, and future implementation of eHealth interventions for caregivers of people with dementia in a municipal context. These insights were applied to tailored implementation strategies to facilitate the future implementation of interventions such as Myinlife and Partner in Balance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".