Evidence for inequities in the risk of foodborne and waterborne diseases in the Canadian population: a scoping review
Bibliographic record
Abstract
Foodborne and waterborne diseases (FWBD) affect over four million Canadians each year. High-risk groups include young children, older adults, immunocompromised individuals, pregnant women, those who travel to endemic areas, and those with limited access to safe drinking water. Although some demographic information is routinely collected, lack of socio-environmental data limits identification of other vulnerable populations. This study aimed to systematically gather and map available evidence for FWBD inequities in Canada and identify knowledge gaps. A scoping review of four academic databases and grey literature was conducted following a prospectively registered protocol. Thirty-five articles met the eligibility criteria. Results were synthesized by PROGRESS-Plus factors. Measuring inequities was a specified objective in 12 studies (34.2%). FWBD occurrence by age (n = 32; 91.4%), sex (n = 28; 80.0%), and place of residence (n = 27; 77.1%) were most studied. No studies assessed gender, language, occupation, religion, or social capital. Differences in study design, study populations, exposure and outcome definitions and measurement, and analysis were substantial. This review identified knowledge gaps including never-studied equity stratifiers, FWB pathogens, and sub-populations. Larger studies with explicit objectives to measure health inequities are needed. Public health professionals and researchers need to expand and standardize collection and linkage of socio-environmental data to FWBD outcomes.
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 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 | 0.016 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.027 | 0.043 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".