Evaluating the Effectiveness of Rural Digital Social Prescribing in Korea: Protocol for a Cohort Study
Bibliographic record
Abstract
BACKGROUND: The UK National Health Service (NHS) has introduced a digital social prescribing (DSP) system to improve the mental health of the aging population. In Korea, an ongoing pilot social prescribing project has been implemented for older individuals in rural areas since 2019. OBJECTIVE: This research aims to develop a DSP program and to evaluate the effectiveness of the digital platform in rural areas of Korea. METHODS: This study was designed as a prospective cohort method for the development and effectiveness evaluation of rural DSP in Korea. The study divided participants into four groups. Group 1 will continuously implement the existing social prescribing program, group 2 implemented the existing social prescribing program but was switched to DSP in 2023, group 3 newly started the DSP, and the remaining group is the control. The research area of this study is Gangwon Province in Korea. The study is being conducted in Wonju, Chuncheon, and Gangneung. This study will use indicators to measure depression, anxiety, loneliness, cognitive function, and digital literacy. In the future, the interventions will implement the digital platform and the Music Story Telling program. This study will evaluate the effectiveness of DSP using difference-in-differences regression and cost-benefit analysis. RESULTS: This study was approved for funding from the National Research Foundation of Korea funded by the Ministry of Education in October 2022. The results of the data analysis are expected to be available in September 2023. CONCLUSIONS: The platform will be spread to rural areas in Korea and will serve as the foundation for effectively managing the feelings of solitude and depression among older individuals. This study will provide vital evidence for disseminating DSP in Asian countries such as Japan, China, Singapore, and Taiwan as well as for studying DSP in Korea. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/46371.
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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 | 0.043 | 0.028 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.046 | 0.007 |
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".