Integrated Care for Latent Tuberculosis Infection (LTBI) at a Primary Health Care Facility for Refugees in Winnipeg, Canada: A Mixed-Methods Evaluation
Bibliographic record
Abstract
Although Canada has one of the lowest tuberculosis incidence rates in the world, certain groups are disproportionately affected, including foreign born people from high incidence countries. The Winnipeg Regional Health Authority has initiated a process to decentralize latent tuberculosis infection (LTBI) management at primary care clinics in Winnipeg. One of these clinics is BridgeCare Clinic which provides services to government-assisted refugees. The present study describes the BridgeCare Clinic LTBI program and reviews program outcomes from January 2015 to October 2016. Refugees at BridgeCare Clinic receive comprehensive care, including LTBI screening and treatment. The LTBI program is managed by physicians, nurse practitioners, and primary care nurses under a patient-centered model of care. An accessible interpretation service, education to clients, and laboratory sampling at the clinic with free IGRA testing are important components of the program. Anonymized data on client outcomes were statistically analyzed and qualitative interviews were conducted with senior staff. During the study period, 274 IGRA tests were ordered with 158 negative results (57.7%) and 101 positive results (36.9%). Of 45 clients eligible (from January to December 2015) for LTBI treatment, 11 (24.4%) declined to receive treatment and 34 (75.6%) started treatment. Twenty-seven (79.4%) clients completed treatment, 3 (8.8%) clients moved out of province, and 4 (11.8%) did not complete treatment. The most recent World Health Organization strategy for tuberculosis control calls for integrated, patient-centered care and prevention. Aligned with these WHO recommendations, our experience suggests that LTBI care and treatment can be delivered effectively in a primary care setting using an integrated patient-centered approach.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".