Integrating tuberculosis research with public health infrastructure: Lessons on community engagement from Orizaba, Mexico
Bibliographic record
Abstract
Background: The Orizaba Health Region, in Veracruz, Mexico, has hosted the research programme of the Consorcio Mexicano contra la Tuberculosis since 1995. Methods: The objective of this retrospective case study conducted in 2009 was to describe and explain the evolution and outcomes of the stakeholder and community engagement activities of the Consorcio . Recorded interviews and focus groups were coded to identify major themes related to the success of stakeholder and community engagement activities. Results: The Consorcio successfully managed to embed its research program into the local public health infrastructure. This integration was possible because the core research team tailored its engagement strategy to the local context, while focusing on a large spectrum of stakeholders with various positions of authority and responsibility. The overall engagement strategy can be described as a three-pronged endeavor: building a “coalition” with local authorities, nurturing “camaraderie” with community health workers, and striving to be “present” in the lives of community members and participants. Conclusions: The Consorcio ’s efforts teach valuable lessons on how to approach stakeholder and community engagement in tuberculosis (TB) research, particularly in developing countries. Furthermore, the health outcomes reveal stakeholder and community engagement as a potentially under-tapped tool to promote disease control.
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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".