Missing men with tuberculosis: the need to address structural influences and implement targeted and multidimensional interventions
Bibliographic record
Abstract
Tuberculosis (TB) is treatable but is the leading infectious cause of death worldwide, with men over-represented in some key aspects of the disease burden. Men's TB epidemiological scenario occurs within a wider public health and historical context, including their prior sidelining in health discussions. Differences are however noticeable in how some Western countries and high TB and HIV burden low and middle-income countries (LMIC) including in Africa have approached the subject(s) of men and health. The former have a comparatively long history of scholarship, and lately are implementing actions targeting men's health and wellness, both increasingly addressing multilevel social and structural determinants. In contrast, in the latter men have received attention primarily for their sexual practices and role in HIV and AIDS and gender-based violence; moreover, interventions, guided by the public health approach, have stressed short-term, measurable and medical goals. Debates and the limited available empirical literature on men's engagement with TB-related healthcare are nevertheless indicating need for a shift, within TB work with men in high burden LMICs towards, structural and multicomponent interventions.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".