SCIENTOMETRIC ANALYSIS OF RESEARCH ON TUBERCULOSIS IN THE HOMELESS POPULATION IN SOUTH AMERICA, 2013 - 2023
Bibliographic record
Abstract
Objective: To describe the scientometric profile of scientific production related to tuberculosis in the homeless population in South America, offering a new perspective on the impact of this disease on this vulnerable population. Methods: This is a bibliographic research based on publications about tuberculosis (TB) in South America, indexed in PubMed, Cochrane, LILACS/Scielo, Web of Science, and embase databases, according to specific criteria, from 2013 to 2023. Data regarding authorship, publication source, collaboration networks, socioeconomic analysis, and term frequency were extracted and analyzed, with a focus on the conceptual structure of the studies. Bibliographic networks were built using the scientometric visualization software VOSviewer® 1.6.16, and data science tools were applied to the generated spreadsheets for the consolidation of the final dataset. Results: A total of 82 publications were included in the study and analyzed according to the established inclusion criteria. Among these, an annual average of 48.6 citations per publication on tuberculosis in the homeless population in South America was observed. Conclusion: The study highlights the still limited number of studies focused on tuberculosis in this specific population. Despite advancements in scientific production, there is a concentration of publication origins in geographical areas with lower disease endemicity. These results point to the need for greater investment aimed at understanding, monitoring, and controlling tuberculosis. The analysis of bibliographic production plays a strategic role in strengthening scientific development and planning disease control programs in vulnerable populations.
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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 | 0.018 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.133 | 0.196 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".