Cultural validation and language translation of the scientific SCI exercise guidelines for use in Indonesia, Japan, Korea, and Thailand
Bibliographic record
Abstract
CONTEXT: Indonesia, Japan, Korea, Thailand. OBJECTIVE: To culturally validate and translate the Scientific Exercise Guidelines for Adults with Spinal Cord Injury (SEG-SCI) for use in four Asian countries. DESIGN: Systematic Review. PARTICIPANTS: N/A. METHODS: A systematic review was conducted to identify all published English- and local-language studies conducted in Indonesia, Japan, Korea, and Thailand, testing the effects of exercise training interventions on fitness and cardiometabolic health in adults with acute or chronic SCI. Protocols and results from high-quality controlled studies were compared with the SEG-SCI. Forward and backward translation processes were used to translate the guidelines into Bahasa Indonesian, Japanese, Korean and Thai languages. RESULTS: Fifteen studies met the review criteria. At least one study from each country implemented exercise prescriptions that met or exceeded the SEG-SCI. Two were controlled studies. In those two studies, relative to control conditions, participants in exercise conditions achieved significant improvements in fitness or cardiometabolic health outcomes only when the exercise intervention protocol met or exceeded the SEG-SCI. During the language translation processes, end-users confirmed that SEG-SCI language and terminology were clear. CONCLUSION: Clinical researchers in Indonesia, Japan, Korea and Thailand have implemented exercise protocols that meet or exceed the SCI-SEG. Results of high-quality studies align with the SEG-SCI recommendations. Based on this evidence, we recommend that the SEG-SCI be adopted in these countries. The cultural validation and translation of the SEG-SCI is an important step towards establishing consistent SCI exercise prescriptions in research, clinical and community settings around the world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.209 | 0.406 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".