Rural digital social innovation for health and social care: A systematic review
Bibliographic record
Abstract
Digitalization in the health sector has created numerous opportunities for social innovators and change-makers. However, there is a lack of integrated knowledge of how the latest technological changes have impacted social innovators from marginalized populations living in rural contexts who are often considered the “left-behind” segment in the age of digitalization. The purpose of this systematic review was to synthesize evidence for rural digital social innovations for health and social care. Drawing on searches from multiple databases we adopted a Context-Process-Outcomes Model to evaluate 25 empirical studies focused on innovations within the healthcare sector (18 studies) and general community level innovations (7 studies). Geographical distance between providers and rural patients was often the context for healthcare innovations, necessitating processes with multiple levels of collaboration, whereas diverse community-specific challenges were usually addressed through grassroots initiatives. Most healthcare and community level innovations had evidence of positive outcomes (e.g., positive impacts on health service utilization or community health). Although digitalization accelerated the scope and reach of social innovations, substantial human investment and rural community engagement remained crucial for success. In conclusion, our application of the Context-Process-Outcomes framework enabled us to aggregate diverse findings and unpack the role of digitalization in rural social innovations.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".