Covid-19 Awareness, Preparedness, and Impact on the Most Vulnerable Groups among the Rohingya Community in Cox's Bazar
Bibliographic record
Abstract
The World Health Organization (WHO) declared the Coronavirus outbreak a global pandemic on March \n11, 2020 (1), resulting in nationwide quarantines and national emergencies. Bangladesh was no exception, \nand in late March 2020, the government implemented a phased nationwide lockdown, officially \nacknowledging the presence of Covid-19 in the Rohingya camps of Cox's Bazar on May 14, 2020 (2). \nBangladesh hosts the largest forcibly displaced population in the world in Cox’s Bazar district with \n855,000 Rohingyas from Myanmar (2). A majority reside in Ukhiya and Teknaf sub-districts in 34 camps, \nalong an estimated 548,000 Bangladeshis who are one of the poorest population groups in the country \nwith 33% living below the poverty line (2). The Covid-19 pandemic poses a range of governance, \ndemographic, environmental, and policy-related challenges an already fragile context. \nTo prevent Covid-19 in Bangladesh and mitigate its impacts, long-term transformative and inclusive \ninterventions that are also sustainable are required, particularly in the context of humanitarian crises. To \nsupport this notion and to explore Covid-19 awareness, preparedness, and impact on the most vulnerable \ngroups (MVGs) among the Rohingya Community in Cox's Bazar, BRAC James P Grant School of Public \nHealth (BRAC JPGSPH), BRAC University is leading this participatory action research project funded by \nthe International Development Research Centre (IDRC), Canada (3) and is working with the \nimplementation partner - Centre for Peace and Justice, BRAC University. The aim of this project is to \nprovide critical evidence to support policies and interventions to mitigate the adverse impacts of \nCovid-19 on the MVGs in the Rohingya community.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".