Exploring barriers to sickle cell disease care in a lower-middle income country – A qualitative exploration of the Tharu perspective in rural Nepal
Bibliographic record
Abstract
Background The indigenous Tharu ethnic group inhabiting the Terai (lowland) region of Nepal has been shown to have a high prevalence of sickle cell disease (SCD); specifically, previous studies have found a prevalence of 9.3% for sickle cell trait in this group. The lack of knowledge about SCD in this population is suspected to be a major barrier in preventing early intervention. This qualitative study aimed to conduct a needs assessment and identify themes relating to the local Tharu population's perceptions of SCD and its related care. Methods Using snowball sampling for recruitment, 133 Tharu community members in the rural district of Dang met inclusion criteria. Researchers conducted 22 focus group discussions (FGDs) using semi-structured interviews in the local Tharu dialect, Nepali, Hindi, or English until topic saturation. A constant comparison analysis based off grounded theory was used for thematic analysis (NVivo 11™). Results Three major themes emerged that explored the Tharu community's views of SCD: the social implications of SCD, the importance of SCD education, and the role of positive interventions. Inequity and lack of access to healthcare services were also thought to contribute to the lack of SCD-specific care. Conclusion Using participatory action-based research to empower underserved communities, we have identified major themes surrounding the lack of understanding and wish for enhanced SCD awareness and education within the Tharu community. Our research explores fundamental barriers that must be addressed to develop a sustainable, accessible, and comprehensive SCD care plan in the region.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".