ArticleHealthcare (Amsterdam, Netherlands)2020
Dynamic Diffusion Network: Advancing moral injury care and suicide prevention using an innovative model.
Article in Healthcare (Amsterdam, Netherlands), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 16 citations in OpenAlex.
- Spiritual Supports for Formerly Unhoused US Veterans: A Quality Improvement Evaluation.Journal of religion and health · 2026Article
- TheFrontiers in psychiatry · 2026Article
- How senior leaders support innovations in large learning health systems: Insights from United States Veterans Health Administration national program office leaders.Learning health systems · 2025Article
- Walk the line: a systemic perspective on stress experienced by emergency medical personnel by comparing military and civilian prehospital settings.Frontiers in public health · 2023Article
- Diffusion of excellence: evaluating a system to identify, replicate, and spread promising innovative practices across the Veterans health administration.Frontiers in health services · 2023Article
- Core Components of Moral Injury Groups Co-Facilitated by Mental Health Providers and Chaplains.Spirituality in clinical practice (Washington, D.C.) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Healthcare providers across a wide variety of settings face a common challenge: the need to provide real time care for complex problems that are not adequately addressed by existing protocols. In response to these intervention gaps, frontline providers may utilize existing evidence to develop new approaches that are tailored to specific problems. It is imperative that such approaches undergo some form of evaluation, ensuring quality control while permitting ongoing adaptation and refinement. "Dynamic diffusion" is an innovative approach to intervention improvement and dissemination whereby care practices are delivered and continuously evaluated under real-world conditions as part of a structured network experience. This "dynamic diffusion network" (DDN) promotes cross-pollination of ideas and shared learning to generate relatively rapid improvements in care. The pilot Mental Health and Chaplaincy DDN was developed to advance suicide prevention efforts and moral injury care practices being conducted by 13 chaplain-mental health professional teams across the Veterans Health Administration. Lessons learned from the pilot DDN include the importance of the following: geographic and cultural diversity among innovation collaborators to ensure the broadest possible relevance of solutions; leadership support to facilitate engagement of frontline providers in quality improvement efforts; and participation in a community of practice to motivate providers and offer opportunities for direct collaboration and cross-pollination of ideas.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.