Evaluating Canada’s initiative of enhanced screening for tuberculosis infection in migrants: Implementation lessons from Alberta
Bibliographic record
Abstract
Background: The domestic tuberculosis (TB) disease burden in high-income, low TB-incidence countries is largely driven by the reactivation of remotely acquired TB infections (TBIs) in people born outside the country (PBOC).In Canada, PBOC now accounts for more than three quarters of annual active TB diagnoses.To prevent some of this disease experience, Immigration, Refugees and Citizenship Canada (IRCC) rolled out a new TBI screening initiative in 2019.Objective: An evaluation of TB outcomes among individuals referred through this initiative between May 2019 and May 2023 in Alberta, Canada.Methods: Inclusion criteria for this initiative are migrants who are required to undergo an immigration medical exam with at least one of HIV/AIDS, solid organ transplant, end-stage renal disease, recent close TB contact (within five years), and past head and neck cancer.Those with a positive screening test for TBI are referred directly to TB services in the stated province/ territory of landing for assessment and treatment.Results: Over four years, 179 referrals were made to Alberta.No one referred through the program and offered treatment developed active TB.Overall, 95 individuals were considered suitable candidates for prevention, among whom 87% accepted.Completion was high at nearly 95%.Inefficiencies included 113 individuals undergoing repeated TBI testing locally, 39 (21.8%) referrals not meeting the inclusion criteria, and 61 (34.1%) individuals being rereferred despite being past patients of Alberta TB services.Conclusion: Our findings highlight that, in Alberta, IRCC's new TBI screening initiative was highly successful in connecting referred individuals to TB services.The initiative experienced some inefficiencies and we describe areas where it could be improved.
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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 | 0.027 | 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 | 0.009 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".