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Record W1987473110 · doi:10.3390/ijerph110201626

Risk Factors Associated with the Choice to Drink Bottled Water and Tap Water in Rural Saskatchewan

2014· article· en· W1987473110 on OpenAlexafffundabout
Lianne McLeod, Lalita Bharadwaj, Cheryl Waldner

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsUniversity of Saskatchewan
FundersSaskatchewan Health Research Foundation
KeywordsBottled waterTap waterEnvironmental healthWater safetyPoison controlMedicineGeographyEnvironmental scienceWater qualityEnvironmental engineeringBiologyEcology

Abstract

fetched live from OpenAlex

A cross-sectional study investigated risk factors associated with choices to drink bottled water and tap water in rural Saskatchewan. Of 7,500 anonymous postal questionnaires mailed out, 2,065 responses were analyzed using generalized linear mixed models. Those who reported a water advisory (p < 0.001) or living in the area for £10 years (p = 0.01) were more likely to choose bottled water. Those who reported tap water was not safe to drink were more likely to choose bottled water, an effect greater for those who had no aesthetic complaints (p ≤ 0.001), while those with aesthetic complaints were more likely to choose bottled water if they believed the water was safe (p < 0.001). Respondents who treated their water and did not use a community supply were more likely to choose bottled water (p < 0.001), while those who did not treat their water were more likely to choose bottled water regardless of whether a community supply was used (p < 0.001). A similar pattern of risk factors was associated with a decreased likelihood of consuming tap water daily; however, the use of a community water supply was not significant. Understanding the factors involved in drinking water choices could inform public health education efforts regarding water management in rural areas.

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)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.044
GPT teacher head0.359
Teacher spread0.315 · 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 designObservational
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

Citations45
Published2014
Admission routes3
Has abstractyes

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