Exploring Rural Community Practices in HIV Management for the Design of Technology for Hypertensive Patients Living with HIV
Bibliographic record
Abstract
Information communication technologies for development (ICTD) can support people with chronic illnesses living in rural communities. In Kenya, ICTD use in areas where undetected cases of hypertension and high HIV infection rates exist is underexplored. Partnering with a health facility in Migori, Kenya, we report on the uses of technology in managing HIV. We see the use of technology to manage HIV was influenced by the roles and routines of patients and clinicians, trust between practitioners and patients, and sources of data that clinicians use for patient examination. We use these results to inform the design of technologies that can support patients living with comorbid HIV and hypertension, as well as their care providers, to manage their care in similar settings. We also reiterate the important mediatory role that community health volunteers (CHVs) can play in the adoption of technology as patients manage their condition(s) once out of hospital.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".