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Record W7036917039

Communicating advisories on the risk of mercury in fish to the Chinese-Canadian community

2009· dissertation· en· W7036917039 on OpenAlexaboutno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2009
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicMedicinal Plant Research
Canadian institutionsnot available
Fundersnot available
KeywordsFocus groupFish consumptionThe InternetMercury (programming language)MERCURY EXPOSUREFish <Actinopterygii>Public health
DOInot available

Abstract

fetched live from OpenAlex

Thirty-four pregnant Chinese-Canadian women, who self-reported eating a minimum of one meal of fish per week, were recruited from four Canada Prenatal Nutrition Program classes across Metro Toronto to take part in five focus groups conducted in Cantonese or Mandarin. Groups were asked 15 semi-structured questions on the participants' fish consumption habits, awareness of advisories, knowledge of mercury, and their response to messages about mercury in fish. Few participants were aware or had knowledge of advisory messages, and most were generally shocked to hear consumption recommendations. Information issued by public health organizations was well received and trusted. Motivation for behavioural change often stemmed from concern for their children. Resources targeting Chinese-Canadians must focus on creating culturally sensitive materials that are offered in Chinese with visual elements to keep the text brief. Using the internet to post information may be a possibility for future investigation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.026
GPT teacher head0.243
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), 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
Published2009
Admission routes1
Has abstractyes

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