The use of an online survey for collecting food exposure information, Foodbook sub-study, February to April 2015
Bibliographic record
Abstract
BACKGROUND: During foodborne illness outbreak investigations, comparing food exposure frequencies of cases to those of a control population can help identify suspect food sources. The Public Health Agency of Canada (PHAC) conducted an online survey between February and April 2015 to collect seven-day food exposures from a convenience sample. The study period overlapped with a national, population-based exposure survey being conducted via telephone using random digit dialling. A subset of the food exposure questions from the telephone-based survey was included in the online survey. OBJECTIVE: The online survey study objectives were to: 1) describe the survey methodology, survey respondents and response behaviour; and 2) determine if the online methodology is an appropriate alternative to telephone surveys by comparing food exposures. METHODS: The online survey link was distributed via email to employees and public health partners, and was promoted on the PHAC website and social media channels. RESULTS: In total 2,100 surveys were completed. The majority of respondents were female, with high income and education, aged 30 to 39 years. The proportion reporting consuming the food items in the online survey was generally higher than those reported in the telephone survey, with a mean difference of 6.0% (95% CI: 4.2, 7.8). CONCLUSION: In an outbreak investigation, the 6.0% bias could make it more difficult to detect a difference between the case and control food exposures. Nevertheless, given the speed of response and lower resource expenditure of online surveys as well as the willing, able and convenient sample, a bias of 6.0% is considered small enough to be acceptable for future surveys.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".