Using information and communication technologies to involve patients and the public in health education in rural and remote areas: a scoping review
Bibliographic record
Abstract
BACKGROUND: Patient and public involvement (PPI) in health education is a practice whereby research and education are carried in collaboration 'with' patients and/or citizens, maintaining their role as a team member or expert. PPI in health education is of great interest for all stakeholders in the field, as it can make program development more relevant to the public and increase its utilization by the target population. However, little is known about how PPI should be implemented in different settings particularly in rural and remote areas. Therefore, a deeper understanding of how PPI works in different environments is needed. We aim to explore how information and communication technologies (ICT) are used for PPI in health education programs in rural and remote areas. METHODS: We performed a scoping review. Two reviewers independently selected 641 studies from five electronic databases. Data were extracted, charted and validated by the senior researcher and study lead. We performed a narrative synthesis to map the literature. RESULTS: Of the initial 641 articles identified, 5 fit the eligibility criteria. Most of the studies targeted community members at large. Consultation and collaboration were the main levels of PPI, which included communities at large and specific at-risk groups. The main forms of ICT used were telephone and Internet, followed by teleconferences, electronic health records, and weblogs. No study measured the effectiveness of ICT for involving patients in health education in rural and remote areas. CONCLUSION: Telephone and Internet were the most frequently used forms of PPI in health education in rural areas with consultation and collaboration as the main levels of PPI there. No study measured the impact of ICT for PPI in health education in rural areas. Due to this, measuring the impact of ICT in rural and remote areas as a means for PPI in health education of medical students, health professionals and patients requires further study.
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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 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".