Consumer acceptance and preference for brown rice—A mixed‐method qualitative study from Nepal
Bibliographic record
Abstract
Background: Brown rice consumption reduces the risk of diabetes. The prevalence of diabetes is increasing in Nepal; however, dietary preference remains for white rice. This study aimed to understand the perception, enablers, barriers, and facilitators of acceptance brown rice at a worksite cafeteria. Methods: We conducted a mixed-method qualitative research among 42 employees of a hospital in central Nepal. The participants tasted and rated the qualities of five different combinations of brown and white rice on a hedonic scale. We conducted eight focus group discussions (FGDs)-four before and four after tasting rice combinations. FGDs were recorded, transcribed, and coded verbatim and analyzed manually using inductive-deductive thematic method. Results: Before tasting, the participants perceived brown rice as poor in quality. After tasting, the participants found that brown rice had better quality and were willing to switch gradually starting with a 25B ratio. Eighty-three percent of participants liked a combination of 25B. Major barriers were poor perception of its quality, tradition, unavailability, lack of awareness of health benefits, and high price. Major facilitators were availability, self and family awareness about the health benefits, knowledge, the brown rice cooking process, serving with side dishes, prior tasting, and gradual substitution of brown rice. Conclusion: We found that brown rice should be promoted stepwise, first as a mixture with white rice and gradually increasing the proportion of brown rice. Brown rice acceptance can be increased by improved knowledge of its nutrition and health benefits, increasing availability, and affordability.
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
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".