INVISIBLE STRUGGLES: EXPLORING CHALLENGES FACED BY WOMEN WITH AMPUTATION IN INDIA
Bibliographic record
Abstract
Women in India, particularly those with amputation, face significant challenges, including but not limited to, unequal prosthetic access and satisfaction, societal discrimination, and the physical and emotional consequences of amputation. These challenges are further exacerbated by gender biases towards access to education and socioeconomic factors, which increases their vulnerability to unemployment and mental health issues. This article emphasizes the urgent need for affordable and customizable prosthetic options tailored to the unique needs of women with amputation, particularly those from low-income backgrounds who often face neglect. Thus, addressing these disparities would significantly enhance their overall well-being and independence. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/44002/33160 How To Cite: Alam J, Joshi A, Mir N, Chawla N, Sagar S. Invisible struggles: Exploring challenges faced by women with amputation in India. Canadian Prosthetics & Orthotics Journal. 2024; Volume 7, Issue 1, No.5. https://doi.org/10.33137/cpoj.v7i1.44002 Corresponding Author: Professor Sushma Sagar,Division of Trauma Surgery and Critical Care, Jai Prakash Narayan Apex Trauma Centre, AIIMS, New Delhi, India.E-Mails: sagar.sushma@gmail.com; dr.sushma@aiims.gov.inORCID ID: https://orcid.org/0000-0002-4700-9868
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".