Ten years of Foodbook: Utilization of Foodbook survey data for research
Bibliographic record
Abstract
Background: Enteric illnesses are a preventable cause of morbidity and healthcare utilization in Canada. To support public health and epidemiological activities, Foodbook was launched in 2014 by the Public Health Agency of Canada to collect representative information on food, water, and animal exposures, food safety knowledge, burden of gastrointestinal illnesses, and sociodemographic information. The aim of this overview was to identify how this valuable data source has been used in the past decade since its launch. Methods: Peer-reviewed and grey literature were identified by applying the search term "Foodbook" to two academic databases and two grey literature sources, respectively. Citations were screened against eligibility criteria. Study information, including study characteristics, module of Foodbook data used, and how Foodbook data was used was extracted and synthesized in tabular format. Results: A total of 27 articles were identified in the published literature that utilized Foodbook survey data in their analyses. The most common use was for outbreak investigations. In addition, Foodbook has been used to describe food, water, and animal exposures, determine food safety knowledge and practices of Canadians, estimate the burden of acute gastrointestinal illness, and evaluate data collection methods for foodborne illnesses. Conclusion: By summarizing its use, the authors aim to encourage broader use of this publicly available data source to inform health protection and promotion activities to reduce the burden of enteric illnesses in Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.034 | 0.075 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".