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Record W2755653713 · doi:10.47339/ephj.2017.79

Evaluation of public health interventions in shellfish tag compliance rates

2017· article· en· W2755653713 on OpenAlexvenueaboutno aff
Nicole Park, Environmental Health BCIT School of Health Sciences, Helen Heacock, Lorraine McIntyre

Bibliographic record

VenueBCIT Environmental Public Health Journal · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Supply Chain Traceability
Canadian institutionsnot available
Fundersnot available
KeywordsShellfishEnvironmental healthOutbreakPsychological interventionFood safetyPublic healthVibrio parahaemolyticusFisheryGeographyMedicineBiologyFish <Actinopterygii>Aquatic animalNursing

Abstract

fetched live from OpenAlex

Background: The recent Vibrio parahaemolyticus outbreak in the summer of 2015 highlighted that shellfish tags are one of the key pieces of information used to trace back and determine the source of a foodborne outbreak or illness associated with raw or uncooked shellfish. According to the Canadian Food Inspection Agency, all shellfish tags must meet the requirements stated in the Section 7.3 of the Canadian Shellfish Sanitation Program (CSSP). Non-compliant tags may hinder national and regional regulatory agencies from identifying problems in harvest locations and at the processors, and further impede provincial control measures. As a result of the national outbreak, the BC Center for Disease Control (BCCDC), Ministry of Agriculture and the Canadian Food Inspection Agency (CFIA), as well as health authorities and Environmental Health Officers (EHO) have been involved in a variety of actions and interventions to improve compliance. These include efforts to promote education and to improve control and surveillance of V. parahaemolyticus and other shellfish associated illnesses. This study examined the effectiveness of health agencies’ interventions to improve shellfish tag compliance rates to Section 7.3 of the CSSP by comparing the numbers of shellfish tags in compliance before and after the interventions that were implemented in 2016. Methods: 120 randomly selected shellfish tags were grouped into “Before” and “After” interventions. By assessing the date of processing, 60 tags collected before September 2016 were placed into the “Before” group. Another 60 tags collected after September 2016 were placed into the “After” group. Within each group, shellfish tags were individually analyzed to determine whether the tag met or exceeded the required quality, information, and type and quantity criteria. Shellfish tags were considered “Compliant” if they completely fulfill 10 components embodied in the criteria, whereas shellfish tags that failed to meet all the components were labeled “Non-compliant”. Results: Based on the statistical analysis conducted on the data, there was a greater proportion of compliant shellfish tags post-intervention compared to pre-intervention. The Pearson’s Chi-square test confirmed that there was a statically significant association (p-value = 0.000) between the numbers of shellfish tags in compliance and the interventions that were implemented after the outbreak. Conclusion: The results have demonstrated that the interventions implemented by numerous regulatory authorities resulted in greater compliance to Section 7.3 of the CSSP. Public health regulators including the Ministry of Agriculture and the CFIA, as well as BCCDC and EHOs should continuously be involved in a variety of actions, such as promoting education at the processor and retail level and also implementing interventions to improve compliance. By doing so, successful interventions and increased compliance rates will lead to rapid identification of shellfish-related illnesses or outbreaks and facilitate control measures that can expeditiously remediate public health issues.

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)0.016
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.743
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.348
GPT teacher head0.391
Teacher spread0.043 · 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.

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
Published2017
Admission routes2
Has abstractyes

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