Generating user-driven patient personas to support preventive health care activities of rural-living unattached patients
Bibliographic record
Abstract
Objective: This study created personas using quantitative segmentation and knowledge user enhancement to inform intervention and service design for rural patients to encourage preventive care uptake. Methods: This study comprised a cross-sectional survey of rural unattached patients and a co-design workshop for persona development. Cross-sectional survey data were analyzed for meaningful subgroups based on quartiles of preventive care completion. These quartiles informed "relevant user segments" grouped according to demographics (age, sex), length of unattachment, percentage of up-to-date preventive activities, health care visit frequency, preventive priorities, communication confidence with providers, and chronic health conditions, which were then used in the workshop to build the final personas. Results: 207 responses informed persona user segments, and five health care providers and 13 patients attended the workshop. The resulting four personas, included John (not up-to-date on preventive care activities), Terrance (few up-to-date preventive care activities), George (moderately up-to-date preventive care activities), and Anne (mostly up-to-date preventive care activities). Conclusion: Quantitative persona development with integrated knowledge user co-design/enhancement elevated and enriched final personas that achieved robust profiles for intervention design. Innovation: This project's use of a progressive methodology to build robust personas coupled with participant feedback on the co-design process offers a replicable approach for health researchers.
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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 | 0.026 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".