Knowledge, attitude, and practice of pesticide use by vegetable growers in Bangladesh: a health literacy perspective in relation to non-communicable diseases
Bibliographic record
Abstract
Pesticides are widely used by vegetable growers in Bangladesh, however the health consequences of these chemicals in relation to non-communicable diseases (NCDs) is largely unknown. NCDs have emerged as a major health concern in recent decades and cause deaths, chronic illnesses, and psychosomatic suffering for people worldwide. In Bangladesh, a lack of health literacy among other reasons contributes to the occurrence and prevalence of NCDs. This study interprets and evaluates the status of key health literacy forming components, such as knowledge, attitude, and practices (KAP) of vegetable growers with respect to the use of pesticides. The study was carried out in six districts of Bangladesh. A multistage sampling procedure was used to obtain a survey sample of 334 farmers who grow vegetables and use pesticides. The results revealed that the level of knowledge of the farmers is poor. They are also not very aware of the relationship between pesticide use and potential vulnerability to NCDs. Such knowledge, along with attitude and practices developed through long-held beliefs and perceptions are not helpful for the safe and appropriate use and application of pesticides. To redress such KAP situations among vegetable growers, we posit that policy actors and stakeholders across public health and agricultural sectors, and developmental agencies must strive to improve health literacy in terms of KAP. Large-scale programmatic interventions in the knowledge, attitude, and practices of vegetable growers through training, education, or mass promotion could enhance their literacy and diminish the unabated use of pesticides.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".