Critical reflections on evidence, ethics and effectiveness in the management of tuberculosis: public health and global perspectives
Bibliographic record
Abstract
BACKGROUND: Tuberculosis is a major cause of morbidity and mortality globally. Recent scholarly attention to public health ethics provides an opportunity to analyze several ethical issues raised by the global tuberculosis pandemic. DISCUSSION: Recently articulated frameworks for public health ethics emphasize the importance of effectiveness in the justification of public health action. This paper critically reviews the relationship between these frameworks and the published evidence of effectiveness of tuberculosis interventions, with a specific focus on the controversies engendered by the endorsement of programs of service delivery that emphasize direct observation of therapy. The role of global economic inequities in perpetuating the tuberculosis pandemic is also discussed. SUMMARY: Tuberculosis is a complex but well understood disease that raises important ethical challenges for emerging frameworks in public health ethics. The exact role of effectiveness as a criterion for judging the ethics of interventions needs greater discussion and analysis. Emerging frameworks are silent about the economic conditions contributing to the global burden of illness associated with tuberculosis and this requires remediation.
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 | 0.344 | 0.437 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.108 |
| Scholarly communication | 0.031 | 0.048 |
| Open science | 0.009 | 0.022 |
| Research integrity | 0.059 | 0.080 |
| Insufficient payload (model declined to judge) | 0.005 | 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".