Study on the Household Use of Iodised Salt in Sindh and Punjab Provinces, Pakistan: Implications for Policy Makers
Bibliographic record
Abstract
Purpose: To assess knowledge, attitude and practices with respect to use of iodised salt, and to estimate its uptake at household level in Sindh and Punjab, Pakistan. Methods: A cross sectional survey was conducted between January and March 2007. A structured questionnaire was administered and household salt tested for iodine content across 9,701 households to identify current knowledge and practices towards use of iodised salt. Results: Nearly 85% of the salt tested had no iodine, 8% had iodine levels of at least 75 ppm, whereas 7% of the salt contained between 15 and 50 ppm of iodine.The results of multivariate logistic regression analysis revealed that in comparison to urban areas, rural households were more likely not to use of iodised salt (adjusted odds ratio (AOR) =1.38, 95% CI 1.16-1.62), and Province Sindh was less likely not to use of iodised salt as compare to Punjab (AOR =0.81, 95% CI 0.69-0.96). In addition, results also revealed that illiteracy (AOR =1.61, 95% CI 1.28-2.04), no knowledge of iodised salt (AOR =2.09, 95% CI 1.44-3.04), unavailability of iodised salt (AOR =2.93, 95% CI 2.10-4.07), and unawareness about the advantages of use of iodised salt (AOR =1.97, 95% CI 1.65-2.36) were the main associated factors with non-use of iodised salt for cooking at household levels in Sindh and Punjab provinces, Pakistan. Conclusions: Despite awareness of iodised salt,actual use of adequately iodised salt was much lower, hence collaborative efforts between public and private sectors are strongly recommended to increase the availability and salt iodization in Pakistan.
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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 | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".