From rehabilitation to prevention: The evolving role of physical therapy in healthcare policy transformation and health equity—A systematic review based on South Korea’s development experience
Bibliographic record
Abstract
With the rise of the global concept of green healthcare, the role of physical therapy in transforming healthcare policies has been further extended to the fields of resource conservation and environmental sustainability. Using a systematic literature review methodology and taking South Korea as a representative case, it systematically analyzes how physical therapy, through non-pharmacological interventions, preventive services, and the application of digital technologies. It found that physical therapy can significantly reduce the consumption of medical resources, decrease the generation of medical waste, and improve service efficiency. It also explores the evolution of laws of physical therapy in terms of policy frameworks, educational approaches, and service delivery models. The research shows that South Korea has made physical therapy the core carrier of environmentally friendly medical interventions by integrating green technologies, optimizing service processes, and reconstructing the primary service network. Thus, it promotes the innovation of healthcare policies towards low-carbon and high-efficiency directions. Driven by the continuous evolution of healthcare policies, physical therapy has transformed from a tool for postoperative rehabilitation into the main body of data-based and legalized public health interventions, participating in health governance, and achieving quantifiable results, and cost-effectiveness in aspects such as accessibility, user awareness, fairness, and effectiveness. These reform practices provide a law-based governance paradigm for global health policy transformation, offering empirical references for countries to optimize healthcare governance frameworks through institutional innovation. Particularly in critical areas such as health equity, well-being in aging populations, and integrated community care, physical therapy demonstrates substantial structural institutional value. This paper recommends that, during healthcare policy transitions, countries should strengthen the institutional integration of physical therapy from multiple dimensions, including legal frameworks, financial support, and professional training, to enhance system resilience and promote comprehensive health and well-being.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".