Community Stakeholder-Driven Technology Solutions Towards Rural Health Equity: A Concept Mapping Study
Bibliographic record
Abstract
Abstract Background Promoting inclusive health and social care for rural populations requires new community-focused innovations, technological infrastructure, creative design thinking, and multi-stakeholder collaboration. Technology holds great potential for promoting health equity for rural populations, who have more chronic illnesses than their urban counterparts but less access to services. Yet, more participatory research approaches are needed to gather community-driven health technology solutions. The purpose of this research was to collaboratively identify and prioritize action strategies for using technology to promote rural health equity through community stakeholder engagement. Methods Concept mapping, a quantitative statistical technique, embedded within a qualitative approach, was used to surface and synthesize technological solutions towards rural health equity from community stakeholders in three steps: 1. idea generation; 2. sorting and rating feasibility/importance; and 3. group interpretation. Purposeful recruitment strategies were used to recruit key stakeholders and organizational representatives from targeted rural communities. Results Overall, 34 rural community stakeholders participated in the concept mapping process. In Step 1, 84 ideas were generated that were reduced to a pool of 30. Multi-dimensional scaling and cluster analysis resulted in a 6-cluster map representing how technological solutions can contribute toward rural health equity. The clusters of ideas included technological solutions and applications, but also ideas to make healthcare more accessible regardless of location, training and support in the use of technology, ensuring digital tools are simplified for ease of use, technologies to support collaboration among healthcare professionals, and ideas for overcoming challenges to data sharing across health systems/networks. Each cluster included ideas and priority areas that were rated as equally important and feasible. Key themes included organizational and individual level solutions, and the development of new technologies while connecting patients to these technologies. Conclusions The concept mapping exercise enabled rural community stakeholders to co-identify technological solutions toward rural health equity. Overall, the grouping of solutions revealed that technological applications require not only access, but also support and collaboration. Concept mapping is a tool that can engage rural community stakeholders in the identification of technological solutions for promoting rural health equity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".