Tuberculosis and Common Mental Disorders: International Lessons for Canadian Immigrant Health Amy Bender, Sepali Guruge,
Bibliographic record
Abstract
Tuberculosis is a pressing global health issue. Its association with other infections, illnesses, and social factors, including immigration, is well known, yet comparatively little research has examined the connections between tuberculosis and mental disorder, particularly among immigrants in Canada. The authors report on a scoping review conducted to better understand the synergies of tuberculosis, mental disorders, and underlying social conditions as they affect immigrants' health. They highlight the articles that focused on the co-occurrence of tuberculosis and depression/anxiety. After describing their approach and strategy, the authors present key thematic categories: prevalence, clinical presentation, and effects of stigma and poverty. Examining the research within the global context, they argue that migration contributes to these synergistic conditions. The review shows that Canadians stand to gain much by learning from low- and middleincome countries about what constitutes best evidence in approaching complex global health issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".