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Record W6948343001 · doi:10.5167/uzh-119151

Examining the public health implications of drinking water-related behaviours and perceptions: A face-to-face exploratory survey of residents in eight coastal communities in British Columbia and Nova Scotia

2015· article· en· W6948343001 on OpenAlexaboutno aff

Bibliographic record

VenueZurich Open Repository and Archive (University of Zurich) · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicFecal contamination and water quality
Canadian institutionsnot available
Fundersnot available
KeywordsNova scotiaPublic healthExploratory researchWater qualityRisk perceptionQuality (philosophy)

Abstract

fetched live from OpenAlex

In Canada the quality of drinking water and its availability are a reflection of where one lives. Coastal communities, which are particularly susceptible to boil water advisories, present an understudied opportunity to understand drinking water–related behaviours and perceptions. How public health practitioners determine actions needed to prevent water-borne illness is a key factor in the public adopting messaging and/or employing behavioural change. This study involved face-to-face surveys with residents in eight coastal communities in British Columbia and Nova Scotia. All communities had recent histories of boil water advisories and/or water shortages. The findings have significant implications for public health practice seeking to reduce the incidence of water-borne diseases. For example, the respondents had a limited sense of risk of exposure to water-borne illness. This serves as a challenge for public health professionals who are tasked with educating residents about the health benefits and risks associated with drinking tap water, wherein coastal residents not concerned with water quality/availability may view this information as unnecessary. Generally, obtaining a deep understanding of place-based knowledge around health-related issues, as done here, has the potential to impact future policy and management-level decisions and lead to meaningful integration of local perspectives.

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)0.002
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 score0.285
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0000.001
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 head0.071
GPT teacher head0.259
Teacher spread0.188 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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