Knowledge and Perceptions of Latent Tuberculosis Infection among Chinese Immigrants in a Canadian Urban Centre
Bibliographic record
Abstract
Background. Since most tuberculosis (TB) cases in immigrants to British Columbia (BC), Canada, develop from latent TB infection (LTBI), treating immigrants for LTBI can contribute to the eradication of TB. However, adherence to LTBI treatment is a challenge that is influenced by knowledge and perceptions. This research explores Chinese immigrants' knowledge and perceptions towards LTBI in Greater Vancouver. Methods. This mixed methods study included a cross-sectional patient survey at BC's Provincial TB clinics and two focus group discussions (FGDs) with Chinese immigrants. Data from FGDs were coded and analyzed in Simplified Chinese. Codes, themes, and selected quotes were then translated into English. Results. The survey identified a mean basic knowledge score: 40.0% (95% CI: 38.3%, 41.7%). FGDs confirmed that Chinese immigrants' knowledge of LTBI was low, and they confused it with TB disease to the extent of experiencing LTBI associated stigma. Participants also expressed difficulties navigating the health system which impeded testing and treatment of LTBI. Online videos were the preferred format for receiving health information. Conclusion. We identified striking gaps in knowledge surrounding an LTBI diagnosis. Concerns of stigma may influence acceptance and adherence of LTBI treatment in Chinese immigrants. Integrating these findings into routine health care is recommended.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".