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Record W4399984870 · doi:10.18280/ijsdp.190611

Is Bottled Water More Reliable Than Tap Water in Mexico? A Case Study of Household Mental Health Conditions in Two States

2024· article· en· W4399984870 on OpenAlexvenueno aff
Mohsen Sanei, Mina Khodadad

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsnot available
FundersKempestiftelsernaLuleå Tekniska Universitet
KeywordsBottled waterTap waterEnvironmental healthMental healthBusinessEnvironmental scienceWater resource managementEnvironmental planningGeographyEnvironmental engineeringMedicine

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.358
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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