Creating a locally driven research agenda for the ethnic minorities of Eastern Myanmar
Bibliographic record
Abstract
BACKGROUND: Research funding and production is inequitably distributed internationally, with emphasis placed on the priorities of funders and international partners. Research capacity development, along with agenda-setting for research priorities can create agency and self-sufficiency and should be inclusive of all relevant stakeholders. Myanmar is a fragile state, where decades of conflict have created a weakened healthcare system and health research sector. The population of Eastern Myanmar have long had their healthcare needs met by community-based organisations and ethnic health organisations operating within Eastern Myanmar and the adjoining Thai-Myanmar border. Despite a transition to civilian rule, the current context does not allow for a truly participatory health research capacity development and agenda-setting exercise between the health leaders of Eastern Myanmar and the government in Yangon. In this context, and with a desire to enhance the capacity, legitimacy and agency of their organisations, the health leaders of Eastern Myanmar are seeking to develop their own health research capacity and to take control of their own research agenda. METHODS: Approximately 60 participants from 15 organisations attended a 3-day forum with the goals of (1) developing research capacity and interest through a research conference and methods workshop; (2) using a nominal group technique (NGT) to develop a locally driven research agenda; and (3) supporting the development of local research projects through ongoing funding and mentorship. RESULTS: Participants were actively engaged in the workshops and NGT. Participants identified a broad range of health issues as priorities and were able to develop consensus around a list of 15 top priorities for the populations they serve. Despite availability of ongoing support, participants did not pursue the opportunity to engage in their own research projects emerging from this forum. CONCLUSIONS: The NGT was an effective way to achieve engagement and consensus around research priorities between a group of healthcare providers, researchers and policy-makers from a variety of ethnic groups. More active involvement of senior leadership must happen before the energy harnessed at such a forum can be implemented in ongoing research capacity development.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.021 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".