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Record W2329613110 · doi:10.2166/washdev.2014.076

Choosing bottled over tapped: drinking water in the Dominican Republic

2014· article· en· W2329613110 on OpenAlexaff
John D. McLennan

Bibliographic record

VenueJournal of Water Sanitation and Hygiene for Development · 2014
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBottled waterResidenceEnvironmental healthWater sourceWater qualityHousehold incomeTap waterBusinessGeographyEnvironmental scienceEnvironmental engineeringWater resource managementDemographic economicsMedicineEconomics

Abstract

fetched live from OpenAlex

While not designated as an improved drinking water source, bottled water is increasingly used by households in low- and middle-income countries as families strive to obtain perceived safer drinking water. The Dominican Republic has high levels of bottled water use despite high levels of piped water access. This study aimed to identify household characteristics that are associated with choosing bottled over tapped drinking water in the Dominican Republic through further examination of data available from a nationally representative Demographic and Healthy Survey from 2007. Among households reporting tapped water as their primary non-drinking water source, 59.6% identified bottled water as their principal drinking water source in comparison with 24.7% identifying tapped water. Greater wealth explained the largest amount of variance in bottled over tapped drinking water. Other hypothesized variables related to choosing bottled over tapped included residence in more urban settings, having a young child in the household, having fewer persons in the household, and a head of household who is female, younger and with higher education. Nationally, representative data which include components investigating perceptions about drinking water and actual quality of drinking water are required to further understand this phenomenon and its impact.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score0.235

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 head0.020
GPT teacher head0.278
Teacher spread0.258 · 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 designNot applicable
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

Citations7
Published2014
Admission routes1
Has abstractyes

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