ArticlePreventing chronic disease2015
Using Concept Mapping to Develop a Strategy for Self-Management Support for Underserved Populations Living With Chronic Conditions, British Columbia, August 2013-June 2014.
Article in Preventing chronic disease, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 6 citations in OpenAlex.
- Article
- Engaging a Community-Academic Partnership to Implement Community-Driven Solutions.Preventing chronic disease · 2025Article
- Designing for Dissemination and Sustainability to Promote Equitable Impacts on Health.Annual review of public health · 2022Review
- Implementation determinants and mechanisms for the prevention and treatment of adolescent HIV in sub-Saharan Africa: concept mapping of the NIH Fogarty International Center Adolescent HIV Implementation Science Alliance (AHISA) initiative.Implementation science communications · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
introductionSelf-management support (SMS) is an essential component of public health approaches to chronic conditions. Given increasing concerns about health equity, the needs of diverse populations must be considered. This study examined potential solutions for addressing the gaps in self-management support initiatives for underserved populations.
methodsStakeholders representing government, nongovernment organizations, Aboriginal communities, health authorities, medical practices, and research institutions generated, sorted, and rated ideas on what could be done to improve self-management support for underserved populations. Concept mapping was used to facilitate the collection and organization of the data and to generate conceptual maps.
resultsParticipants generated 92 ideas that were sorted into 11 clusters (foster partnerships, promote integrated community care, enhance health care provider training, shift government policy, support community development, increase community education, enable client engagement, incorporate client support systems, recognize client capacity, tailor self-management support programs, and develop client skills, training, and tools) and grouped into system, community, and individual levels within a partnership framework.
conclusionThe strategy can stimulate public health dialogue and be a roadmap for developing SMS initiatives. It has the potential to address SMS and chronic condition inequities in underserved populations in several ways: 1) by targeting populations that have greater inequities, 2) by advocating for shifts in government policies that create and perpetuate inequities, 3) by promoting partnerships that may increase the number of SMS initiatives for underserved groups, and 4) by promoting training and engagement that increase the relevance, uptake, and overall effectiveness of SMS.
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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.