Genetic counseling in the context of Bangladesh: current scenario, challenges, and a framework for genetic service implementation
Bibliographic record
Abstract
With the advancements in genetics and genomics in the twenty-first century, genetic services have become an integral part of medical practices in high-income and upper-middle-income countries. However, people living in low and lower-middle-income countries (LICs and LIMCs), including Bangladesh, are rather underprivileged in receiving genetic services. Consequently, genetic disorders are emerging as a significant public health concern in these countries. Lack of expertise, high expense, the dearth of epidemiological data, insufficiently updated medical education system, poor infrastructure, and the absence of comprehensive health policies are the main factors causing people living in these countries not having access to genetic services. In this article, the authors took benefit from their professional experience of practicing medical genetics in the area and reviewed existing literature to provide their opinions. Particularly, it reviews the current knowledge of genetic disorders' burden and their causative factors in Bangladesh. It focuses on why providing genetic services is challenging in the context of the country's cultural and religious sentiment. Finally, it proposes a physician-academician collaborative framework within the existing facility that aims to tackle the challenges. Such a framework could also be useful for other LICs and LMICs to address the challenges associated with providing genetic services.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".