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Record W2087894549 · doi:10.2166/wh.2014.126

Microbial water quality communication: public and practitioner insights from British Columbia, Canada

2014· article· en· W2087894549 on OpenAlexafffundabout
G. Dunn, Natalie Henrich, Bev Holmes, Leila M. Harris, Natalie Prystajecky

Bibliographic record

VenueJournal of Water and Health · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsCentre for Advancing Health OutcomesSt. Paul's HospitalUniversity of British Columbia
FundersGenome British ColumbiaGenome Canada
KeywordsPublic healthRecreationGovernment (linguistics)Water qualityBusinessQuality (philosophy)Work (physics)Public relationsRisk communicationEnvironmental planningInformation sharingHealth communicationEnvironmental resource managementEnvironmental healthPolitical scienceGeographyEcologyEngineeringMedicineEnvironmental scienceRisk analysis (engineering)Biology

Abstract

fetched live from OpenAlex

This work examines the communication interactions of water suppliers and health authorities with the general public regarding microbial source water quality for recreational and drinking water. We compare current approaches to risk communication observable in British Columbia (BC), Canada, with best practices derived from the communications literature, finding significant gaps between theory and practice. By considering public views and government practices together, we identify key disconnects, leading to the conclusion that at present, neither the public's needs nor public health officials' goals are being met. We find: (1) there is a general lack of awareness and poor understanding by the public of microbial threats to water and the associated health implications; (2) the public often does not know where to find water quality information; (3) public information needs are not identified or met; (4) information sharing by authorities is predominantly one-way and reactive (crisis-oriented); and (5) the effectiveness of communications is not evaluated. There is a need for both improved public understanding of water quality-related risks, and new approaches to ensure information related to water quality reaches audiences. Overall, greater attention should be given to planning and goal setting related to microbial water risk communication.

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score0.541

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 studies0.0010.000
Scholarly communication0.0010.001
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 head0.043
GPT teacher head0.316
Teacher spread0.273 · 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

Citations10
Published2014
Admission routes3
Has abstractyes

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