A qualitative program evaluation of the Publicly Available International Foodborne Outbreak Database
Bibliographic record
Abstract
BACKGROUND: The Publicly Available International Foodborne Outbreak Database (PAIFOD) is a regularly updated repository that contains international outbreak data collected from multiple surveillance systems and sources. As of February 2020, the database contained more than 13,000 entries spanning over 20 years. PAIFOD is the only known database that captures international foodborne outbreak data. OBJECTIVE: To explore user perceptions and identify potential directions for PAIFOD and make recommendations for databases with food safety information. METHODS: Between January and March 2020, 16 semistructured telephone interviews were conducted with 24 previous, current and potential PAIFOD users. Interviewees were asked about their knowledge of and experience of using PAIFOD as well as about its strengths and limitations and recommendations for the database. An inductive thematic analysis approach was used to analyze qualitative data and generate themes. RESULTS: Four main themes were generated based on the 24 interviewees' accounts of their experience with and recommendations for PAIFOD: participants viewed PAIFOD as a useful tool; they weren't familiar with its contents or purpose; they stated it should become an open-access platform or linked with another information-sharing initiative; and they considered that PAIFOD had the potential to enhance the Agency's reputation by becoming widely recognized and used. CONCLUSION: This work, along with the ever-changing landscape of foodborne surveillance, supports the need to ensure that PAIFOD is updated to meet the modern-day demands of food safety experts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".