Exploring vocational outcomes, quality of life, and social inclusion in patients with spinal cord injuries following vocational rehabilitation in India
Bibliographic record
Abstract
Abstract Study design We conducted a mixed-methods sequential explanatory study. In India, most spinal cord injuries (SCI) occur within low socioeconomic status populations, typically resulting in poor vocational outcomes post-injury and difficulty reintegrating into the community. This study will increase our understanding of how vocational rehabilitation affects patients with SCI. Objectives This study aims to understand the factors affecting vocational outcomes, quality of life and social inclusion for patients who have completed the Amar Seva Sangam (ASSA’s) rehabilitation program, by examining both quantitative and qualitative measures. Methods Conducted at the University of Toronto, we used self-administered questionnaires via REDCap for quantitative data collection and semi-structured interviews for qualitative data collection to capture aspects of lived SCI experience for five participants. Results Thirty-two participants completed the quantitative phone questionnaire of which 17 were paraplegic and 15 were quadriplegic. Four themes emerged including physical barriers to employment, social inclusion of SCI patients, low income, and state of mental health. Conclusion This study provided a detailed examination of demographic information and lived experiences of ASSA participants. The findings will be relevant and applicable to both clinical and public health sectors in SCI rehabilitation in India and other low- and middle-income countries by directing rehabilitation programs to better address areas of function that allow patients to find success following rehabilitation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".