From knowing our needs to enacting change: findings from community consultations with indigenous communities in Bangladesh
Bibliographic record
Abstract
INTRODUCTION: Indigenous peoples are among the most marginalized peoples in the world due to issues relating to well-being, political representation, and economic production. The research consortium Goals and Governance for Global Health (Go4Health) conducted a community consultation process among marginalized groups across the global South aimed at including their voices in the global discourse around health in the post-2015 development agenda. This paper presents findings from the consultations carried out among indigenous communities in Bangladesh. METHODS: For this qualitative study, our research team consulted the Tripura and Mro communities in Bandarban district living in the isolated Chittagong Hill Tracts region. Community members, leaders, and key informants working in health service delivery were interviewed. Data was analyzed using thematic analysis. FINDINGS: Our findings show that remoteness shapes the daily lives of the communities, and their lack of access to natural resources and basic services prevents them from following health promotion messages. The communities feel that their needs are impossible to secure in a politically indifferent and sometimes hostile environment. CONCLUSION: Communities are keen to participate and work with duty bearers in creating the conditions that will lead to their improved quality of life. Clear policies that recognize the status of indigenous peoples are necessary in the Bangladeshi context to allow for the development of services and infrastructure.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".