Using Concept Mapping to Develop a Strategy for Self-Management Support for Underserved Populations Living With Chronic Conditions, British Columbia, August 2013–June 2014
Bibliographic record
Abstract
INTRODUCTION: Self-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. METHODS: Stakeholders 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. RESULTS: Participants 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. CONCLUSION: The 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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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".