Low prioritization of latent tuberculosis infection—<scp>A</scp> systemic barrier to tuberculosis control: <scp>A</scp> qualitative study in <scp>O</scp>ntario, <scp>C</scp>anada
Bibliographic record
Abstract
BACKGROUND: Eliminating tuberculosis (TB) in low-incidence countries is an important global health priority, and Canada has committed to achieve this goal. The elimination of TB in low-incidence countries requires effective management and treatment of latent tuberculosis infection (LTBI). This study aimed to understand and describe the system-level barriers to LTBI treatment for immigrant populations in the Greater Toronto and Hamilton Area, Ontario, Canada. METHODS: A qualitative study that used purposive sampling to recruit and interview health system advisors and planners (n = 10), providers (n = 13), and clients of LTBI health services (n = 9). Data were recorded, transcribed verbatim, and analyzed using content analysis. RESULTS: Low prioritization of LTBI was an overarching theme that impacted four dimensions of LTBI care: management, service delivery, health literacy, and health care access. These factors explained, in part, inequities in the system that were linked to variations in health care quality and health care access. While some planners and providers at the local level were attempting to prioritize LTBI care, there was no clear pathway for information sharing. CONCLUSIONS: This multiperspective study identified barriers beyond the typical socioeconomic determinants and highlighted important upstream factors that hinder treatment initiation and adherence. Addressing these factors is critical if Canada is to meet the WHO's global call to eradicate TB in all low incidence settings.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".