Perspectives on multisectoral accountability framework to end tuberculosis in the Eastern Europe and Central Asia region: a mixed-methods study
Bibliographic record
Abstract
BACKGROUND: Sectors beyond health are essential to combatting a social disease such as tuberculosis (TB). The engagement of the community and civil society sector in Eastern Europe and Central Asia was assessed as part of a broader baseline assessment of multisectoral engagement in national TB responses. METHODS: This was a mixed-methods community-based study. Surveys, interviews, and focus groups were conducted online with TB-engaged community and civil society representatives in Belarus, Kazakhstan, Moldova, Tajikistan, and Ukraine from January to June 2021. Quantitative data, analyzed using descriptive statistics, were triangulated with thematic qualitative analysis. A multisectoral accountability framework and community, rights, and gender framework for TB were used to triangulate the findings and inform data interpretations. RESULTS: Participants (n = 160) included leads, service providers from 74 organizations, and TB survivors. Of 53 survey respondents, most (n = 41, 77·4%) indicated strong/complete agreement to participating in TB service delivery and gender, stigma, and/or legal assessments (n = 27, 50·9%) and research processes (n = 30, 56·6%). However, few indicated inclusion in operational planning and budgeting (n = 13, 24·5%), or political and program impact of community-led monitoring (n = 16, 30·2%), and almost none (n = 2, 3.8%) confirmed dedicated budgets for their TB-related work. Inquiry into the dimensions and criteria for multisectoral actions and accountability revealed their key, yet limited, role in attending to social determinants, with wider engagement hindered by precarious funding. Several organizations balanced building partnerships with other sectors engaged in the TB response against advocacy activities. Inherent obligations toward TB-affected communities were at times overshadowed by obligations to donors and state actors. Coordinating bodies for donor funds, which were multisectoral by design, presented an opportunity to bolster accountability actions within the TB response. CONCLUSIONS: Multisectoral engagement and accountability for TB are a laudable and necessary goal to end TB. Sustainable mechanisms to support the meaningful involvement of TB-affected communities and civil society are needed, particularly in the context of donor transitions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".