Can evidence-based health policy from high-income countries be applied to lower-income countries: considering barriers and facilitators to an organ donor registry in Mumbai, India
Bibliographic record
Abstract
BACKGROUND: Organ transplantation has become an effective means to extend lives; however, a major obstacle is the lack of availability of cadaveric organs. India has one of the lowest cadaver organ donation rates in the world. If India could increase the donor rate, the demand for many organs could be met. Evidence from high-income countries suggests that an organ donor registry can be a valuable tool for increasing donor rates. The purpose of this study is to determine whether the implementation of an organ donor registry is a feasible and appropriate policy option to enhance cadaver organ donation rates in a lower-income country. METHODS: This qualitative policy analysis employs semi-structured interviews with physicians, transplant coordinators, and representatives of organ donation advocacy groups in Mumbai. Interviews were designed to better understand current organ donation procedures and explore key informants' perceptions about Indian government health priorities and the likelihood of an organ donor registry in Mumbai. The 3-i framework (ideas, interests, and institutions) is used to examine how government decisions surrounding organ donation policies are shaped. RESULTS: Findings indicate that organ donation in India is a complex issue due to low public awareness, misperceptions of religious doctrines, the need for family consent, and a nation-wide focus on disease control. Key informants cite social, political, and infrastructural barriers to the implementation of an organ donor registry, including widely held myths about organ donation, competing health priorities, and limited hospital infrastructure. CONCLUSIONS: At present, both the central government and Maharashtra state government struggle to balance international pressures to improve overall population health with the desire to also enhance individual health. Implementing an organ donor registry in Mumbai is not a feasible or appropriate policy option in India's current political and social environment, as the barriers, identified through the 3-i framework lens, may be too difficult to overcome. Despite the evidence supporting the use of donor registries as a means to enhance organ donation rates, it is clear that context is critical and that it is not always practical to apply evidence-based policy solutions from high-income countries to lower-income settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.155 | 0.209 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".