Health of people with spinal cord injury in Singapore: implications for rehabilitation planning and implementation
Bibliographic record
Abstract
PURPOSE: This study aimed to provide a broad overview of the health of people with spinal cord injury (SCI) in Singapore, so as to highlight areas of potential need. These areas could then guide future research and rehabilitation programme development. METHODS: Demographic data, injury information and information about SCI-related secondary impairments, chronic conditions and their associated risk factors, medical and hospital utilisation, participation (Craig Handicap Assessment and Reporting Technique) and life satisfaction (Satisfaction with Life Scale) were collected via interviews from people living with traumatic SCI. RESULTS: On average, participants (50 men and 5 women) were aged 48.3 ± 16.54 years and had had their SCIs for 5 years. -75% with tetraplegia. The most prevalent SCI-related secondary impairments were pain, spasms, bladder problems, bowel problems and oedema. Chronic conditions and their associated risk factors were prevalent. Participation and life satisfaction scores were lower than those reported for similar populations cross-culturally. CONCLUSION: The study revealed several health areas that may be affecting the overall health of people with SCI in Singapore. By focusing on community reintegration and health promotion, physiotherapists and other rehabilitation professionals may augment health outcomes and improve the quality of life of this population in Singapore.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".