Antibiotic dispensation rates among participants in community-driven health research projects in Arctic Canada
Bibliographic record
Abstract
BACKGROUND: Community-driven projects that aim to address public concerns about health risks from H. pylori infection in Indigenous Arctic communities (estimated H. pylori prevalence = 64%) show frequent failure of treatment to eliminate the bacterium. Among project participants, treatment effectiveness is reduced by antibiotic resistance of infecting H. pylori strains, which in turn, is associated with frequent exposure to antibiotics used to treat other infections. This analysis compares antibiotic dispensation rates in Canadian Arctic communities to rates in urban and rural populations in Alberta, a southern Canadian province. METHODS: Project staff collected antibiotic exposure histories for 297 participants enrolled during 2007-2012 in Aklavik, Tuktoyaktuk, and Fort McPherson in the Northwest Territories, and Old Crow, Yukon. Medical chart reviews collected data on systemic antibiotic dispensations for the 5-year period before enrolment for each participant. Antibiotic dispensation data for urban Edmonton, Alberta (average population ~ 860,000) and rural northern Alberta (average population ~ 450,000) during 2010-2013 were obtained from the Alberta Government Interactive Health Data Application. RESULTS: Antibiotic dispensation rates, estimated as dispensations/person-years (95% confidence interval) were: in Arctic communities, 0.89 (0.84, 0.94); in Edmonton, 0.55 (0.55, 0.56); in rural northern Alberta, 0.63 (0.62, 0.63). Antibiotic dispensation rates were higher in women and older age groups in all regions. In all regions, the highest dispensation rates occurred for β-lactam and macrolide antibiotic classes. CONCLUSIONS: These results show more frequent antibiotic dispensation in Arctic communities relative to an urban and rural southern Canadian population.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".