BRIEF REPORTS Financial Aid for the Rehabilitation of Individuals with Spinal Cord Injuries in Bangladesh
Bibliographic record
Abstract
In Bangladesh, a majority of individuals with spinal cord injuries (SCI) are either poor or very poor. In most cases, their families undergo extreme hardship as they lose the income of the only or main earning member, and are unable to bear costs of rehabilitation. Purpose: This mixed method study explored perspectives of individuals with SCI regarding financial aid in the form of interest-free loans for their treatment and rehabilitation at the Centre for the Rehabilitation of the Paralysed (CRP). Method: In the first part of the study, 10 semi-structured face-to-face interviews were conducted. In the second part of the study, 40 persons with SCI were surveyed. The qualitative method involved selection of respondents according to their age, sex and severity of disability. Quantitative interviews were conducted with all persons with SCI in the final stage of hospital rehabilitation, in the third quarter of 2008. results: Though CRP provides financial support depending on the individual’s economic status, many persons with SCI needed additional funding from multiple sources, such as savings (42.5%), mortgaging assets (12.5%), selling assets (45%), receiving loans (37.5%), begging for money (42.5%), and receiving donations from relatives (47.5%) or the community (30%), to meet the direct and indirect costs of rehabilitation. Majority (85%) of those interviewed wanted to receive financial aid in the form of interest-free loans. 76.4% of them wanted the loans to be disbursed in two phases; initially, to offset some of their costs during rehabilitation at CRP, and thereafter, for economic
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".