Healthcare use for acute gastrointestinal illness in two Inuit communities: Rigolet and Iqaluit, Canada
Bibliographic record
Abstract
BACKGROUND: The incidence of self-reported acute gastrointestinal illness (AGI) in Rigolet, Nunatsiavut, and Iqaluit, Nunavut, is higher than reported elsewhere in Canada; as such, understanding AGI-related healthcare use is important for healthcare provision, public health practice and surveillance of AGI. OBJECTIVES: This study described symptoms, severity and duration of self-reported AGI in the general population and examined the incidence and factors associated with healthcare utilization for AGI in these 2 Inuit communities. DESIGN: Cross-sectional survey data were analysed using multivariable exact logistic regression to examine factors associated with individuals' self-reported healthcare and over-the-counter (OTC) medication utilization related to AGI symptoms. RESULTS: In Rigolet, few AGI cases used healthcare services [4.8% (95% CI=1.5-14.4%)]; in Iqaluit, some cases used healthcare services [16.9% (95% CI=11.2-24.7%)]. Missing traditional activities due to AGI (OR=3.8; 95% CI=1.18-12.4) and taking OTC medication for AGI symptoms (OR=3.8; 95% CI=1.2-15.1) were associated with increased odds of using healthcare services in Iqaluit. In both communities, AGI severity and secondary symptoms (extreme tiredness, headache, muscle pains, chills) were significantly associated with increased odds of taking OTC medication. CONCLUSIONS: While rates of self-reported AGI were higher in Inuit communities compared to non-Inuit communities in Canada, there were lower rates of AGI-related healthcare use in Inuit communities compared to other regions in Canada. As such, the rates of healthcare use for a given disease can differ between Inuit and non-Inuit communities, and caution should be exercised in making comparisons between Inuit and non-Inuit health outcomes based solely on clinic records and healthcare use.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".