Evaluation of the public’s knowledge, attitude, and practice on seafood contaminants
Bibliographic record
Abstract
Background: The public perceives seafood generally as a healthy food. Studies have shown that consumption of fish is associated with healthy heart function. However, the benefits of consuming seafood may also come with some risks, which may not be well-known by the public. Seafood can potentially contain contaminants that originate from the natural environment or pollutants from human activity. The contaminants of interest that were focused on in this study include lead, mercury, organophosphates, and domoic acid. Methods: The study utilized a KAP (Knowledge, Attitude, and Practice) survey to evaluate the knowledge, attitude, and practices regarding these contaminants between the general public and those working in the seafood industry. Nominal data was analyzed by the chi-square test while numerical data was analyzed by the t-test. Results: The data obtained did not show a statistically significant difference between the general public and the seafood industry (p-values greater than significance level of 0.05 on all parameters) in their knowledge, attitude, and practice regarding seafood contaminants. Conclusion: There was no difference between the general public and the seafood industry in their knowledge, attitude, and practice regarding seafood contaminants. Although the attitude data was not significant, the effects of some chemical contaminants (organophosphates and domoic acid) were generally incorrectly perceived by both groups unlike biological contaminants. Additional research will be required, but results from this study show that educational intervention by the government or health authorities may be needed.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".