Community stakeholder‐driven technology solutions towards rural health equity: A concept mapping study in Western Canada
Bibliographic record
Abstract
BACKGROUND: 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 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 identify and integrate 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 from western Canada (76% female, mean age = 55.4 years) participated in the concept mapping process. In Step 1, 84 ideas were generated that were reduced to a pool of 30. Multidimensional scaling and cluster analysis resulted in a six-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 health care 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 that were rated as equally important and feasible. Key themes included organizational and individual-level solutions and connecting patients to newly developed technologies. CONCLUSIONS: 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. PATIENT OR PUBLIC CONTRIBUTION: Rural community stakeholders were involved in the generation and interpretation of technological solutions towards rural health equity in a three-step process: (1) individual brainstorming of ideas, (2) sorting and rating all ideas generated and (3) collective interpretation and group consensus on final results.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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".