The Landscape of Health Technology for Equity Deserving Groups in Rural Communities: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Equity-deserving groups face well-known health disparities that are exacerbated by rural residence. Health technologies have shown promise in reducing disparities among these groups, but there has been no comprehensive evidence synthesis of outcomes. PURPOSE: The purpose of this systematic review was to examine the patient, healthcare, and economic outcomes of health technology applications with rural living equity-deserving groups. RESEARCH DESIGN: The databases searched included Medline and Embase. Articles were assessed for bias using the McGill mixed methods appraisal tool. ANALYSIS: Data were synthesized narratively using a convergent integrated approach for qualitative and quantitative findings. RESULTS: = 21) that reported on health technologies targeting rural equity-deserving groups. Overall, patient outcomes - knowledge, self-efficacy, weight loss, and clinical indicators - improved. Healthcare access improved with greater convenience, flexibility, time and travel savings, though travel was still occasionally necessary. All studies reported satisfaction with health technologies. Technology challenges reported related to connectivity and infrastructure issues influencing appointment quality and modality options. While some studies reported additional costs, overall, studies indicated cost savings for patients. CONCLUSIONS: There is a paucity of research on health technologies targeting rural equity-deserving groups, and the available research has primarily focused on women. While current evidence was primarily of high quality, research is needed inclusive of equity-deserving groups and interventions co-designed with users that integrate culturally sensitive approaches. Review registered with Prospero ID = CRD42021285994.
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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 | 0.068 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".