Kidney Sellers From a Village in Nepal: Protocol for an Ethnographic Study
Bibliographic record
Abstract
BACKGROUND: Kidney selling is a global phenomenon, with higher-income countries functioning as recipients and lower-income countries as donors, reflecting the gaps due to poverty and vulnerability. In recent years, an increasing number of residents in a village near the capital city of Nepal have been selling their kidneys; however, the factors embedded in the local social, cultural, political, and individual context driving kidney selling are poorly understood. OBJECTIVE: The aim of this study is to explore the drivers of kidney selling and its consequences in Hokse village in central Nepal, using ethnographic methods and multistakeholder consultations. METHODS: An ethnographic approach will be adopted along with in-depth interviews and key informant interviews among the residents and kidney sellers in the village. Relevant participants in the village will be selected purposively using a snowball approach. The number of participants will be predicated on the principles of data saturation. In addition, consultations with relevant stakeholders will be conducted at various levels, which will include authorities within and outside the village, and policymakers. All interviews will be conducted face to face, audio-recorded for transcription, and subjected to a thematic analysis. RESULTS: This study was approved by Mahidol University Central Institutional Review Board (MU-CIRB 2020/217.1808) in September 2020 and by Nepal Health Research Council (NHRC 716/2020 PhD) in January 2021. The fieldwork started in February 2021 and the data analysis was completed in September 2021. CONCLUSIONS: This study is expected to provide insight into the reasons underlying the practice of kidney selling based on the example of Hokse village, along with the perspectives of multiple stakeholders. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/29364.
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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 | 0.029 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.057 | 0.011 |
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".