Difficulties faced by older Rohingya (forcibly displaced Myanmar nationals) adults in accessing medical services amid the COVID-19 pandemic in Bangladesh
Bibliographic record
Abstract
BACKGROUND: COVID-19 has seriously disrupted health services in many countries including Bangladesh. This research aimed to explore whether Rohingya (forcefully displaced Myanmar nationals) older adults in Bangladesh faced difficulties accessing medicines and routine medical care services amid this pandemic. METHODS: This cross-sectional study was conducted among 416 Rohingya older adults aged 60 years and above residing in Rohingya refugee camps situated in the Cox's Bazar district of Bangladesh and was conducted in October 2020. A purposive sampling technique was followed, and participants' perceived difficulties in accessing medicines and routine medical care were noted through face-to-face interviews. Binary logistic regression models determined the association between outcome and explanatory variables. RESULTS: Overall, one-third of the participants reported difficulties in accessing medicines and routine medical care. Significant factors associated with facing difficulties accessing medicine included feelings of loneliness (adjusted OR (AOR) 3.54, 95% CI 1.93 to 6.48), perceptions that older adults were at the highest risk of COVID-19 (AOR 3.35, 95% CI 1.61 to 6.97) and required additional care during COVID-19 (AOR 6.89, 95% CI 3.62 to 13.13). Also, the notable factors associated with difficulties in receiving routine medical care included living more than 30 min walking distance from the health centre (AOR 3.57, 95% CI 1.95 to 6.56), feelings of loneliness (AOR 2.20, 95% CI 1.25 to 3.87), perception that older adults were at the highest risk of COVID-19 (AOR 2.85, 95% CI 1.36 to 5.99) and perception that they required additional care during the pandemic (AOR 4.55, 95% CI 2.48 to 8.35). CONCLUSION: Many Rohingya older adults faced difficulties in accessing medicines and routine medical care during this pandemic. This call for policy-makers and relevant stakeholders to re-assess emergency preparedness plans including strategies to provide continuing care.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".