Consent for Organ Donation in India: Factors, Challenges, and Opportunities – A Review
Bibliographic record
Abstract
India’s organ donation rate remains under 1 per million population, markedly lower than the consent rates observed in Canada and Spain. This narrative review synthesizes evidence from Indian and global literature to explore systemic, sociocultural, and individual factors influencing consent for deceased organ donation in India. Systemic barriers include delayed referrals, inadequate hospital infrastructure, and a shortage of trained transplant coordinators. Sociocultural factors, such as family decision-making under the Transplantation of Human Organs Act (1994), religious misconceptions, and concerns about bodily integrity, significantly hinder consent. Individual factors, including low awareness and a knowledge–action gap despite altruistic intentions, impede progress. Comparative analysis with Canada and Spain highlights the efficacy of structured referral systems and presumed consent models. We recommend mandatory physician training and the adoption of uniform hospital protocols. In addition, culturally sensitive awareness campaigns led by religious leaders and establishment of a national donor registry are essential. Bridging India’s organ donation gap requires addressing these barriers at multiple levels. Coordinated medical, cultural, and policy reforms will be essential.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".