Is Bottled Water More Reliable Than Tap Water in Mexico? A Case Study of Household Mental Health Conditions in Two States
Bibliographic record
Abstract
Most Mexicans do not trust the water provided by the public network to be healthy enough to drink. This has made Mexico a key consumer of bottled water worldwide. Besides the inadequate quality of water and health concerns, there are other studied reasons for promoting bottled water usage among Mexicans, such as debilitated regulatory frameworks and the power of multinational corporations. Therefore, an argument arises of how much the Mexicans' distrust of the public water network is based on the actual quality of water. This article contributes to this argument by analyzing national household survey data. The association between the two dominant types of drinking water (containers/bottles and public tap water) and mental health conditions (remembering difficulty, depression, and nervousness) are studied in Chihuahua and Nuevo León states, where the usage rates of the two water types are the closest (to minimize biased results). Our results illustrate different conditions for the states demonstrating that, considering mental health conditions, not trusting the quality of public network water in all Mexican regions may not be appropriate. Nevertheless, there might be rightful health concerns in some regions. The outcomes are helpful for authorities to prioritize policies to address water quality management/education actions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".