Using participatory epidemiology to assess factors contributing to common enteric pathogens in Ontario: results from a workshop held at the Ontario Veterinary College, University of Guelph, Ontario
Bibliographic record
Abstract
BACKGROUND: Common enteric pathogens that cause gastrointestinal illness are transmitted to humans through food, water or direct contact. This poses a significant concern to public health as enteric pathogens can cause disease in a large number of people, and cost a substantial amount to treat and prevent. In order to gain a better understanding of the occurrence of enteric disease in Ontario, this study explored public health professionals' perceptions of major contributing factors for common enteric pathogens. METHODS: A case study was conducted as part of a two week training workshop in Participatory Epidemiology held at the Ontario Veterinary College, University of Guelph, in May 2013. Eight semi-structured interviews and four focus groups were conducted with representatives from the Public Health Agency of Canada, the University of Guelph, and three health regions in Southern Ontario. Written notes and pictures captured the qualitative information provided. Results were then analyzed using the mixed methods techniques of triangulation, convergence, and paradox. RESULTS: A total of fifty factors that contribute to enteric disease were identified across all interviews and focus groups. These contributing factors were grouped into key themes (travel, food handling, industry (farm-to-fork), water, geography, demographics, and behaviours) and were categorized as either a risk factor or susceptibility factor. Informants emphasized the complex relationships between the identified factors, and highlighted why these complexities make it difficult to determine where and how a person most likely acquired an enteric pathogen. Workshop participants observed differences in the type and quality of information collected during interviews and focus groups; we hypothesize that this may be attributed to the dynamics between group members (i.e. focus group discussions) as opposed to one-on-one interviews. CONCLUSIONS: The information gathered will serve as a starting point to further explore contributing factors for common enteric pathogens. The identified complexities would be best explored by conducting additional surveillance, as well as interviews and focus groups with a more diverse group of stakeholders. This type of qualitative study can enhance knowledge of enteric pathogen surveillance and contribute to the development of resources and initiatives to holistically address the occurrence of gastrointestinal illness.
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".