Bibliographic record
Abstract
Background Various epidemiological studies indicates that emotional & behavior problems in children are equally prevalent in developing & developed countries. In a developing country such as Bangladesh the daily livelihood & combating common childhood diseases are the foremost concern, and the mental health of children is often neglected. Objectives This study will review an overview of child psychiatry in a developing country, focusing on the lack of existing infrastructure, challenges facing children mental health services & what steps to be taken to develop mental health services in Bangladesh. Methods Critical review of various literature on the topic. Results Child psychiatry is gravely under recognized & underdeveloped in Bangladesh. There is lack of basic infrastructure in the field of child psychiatry. Top most mental disorders amongst children in Bangladesh are behavior disorders & anxiety disorders. These problems are not just restricted to urban regions of Bangladesh; there is also significant levels of emotional problems amongst children in rural areas of Bangladesh. Conclusion Poverty, malnutrition, illiteracy, & in recent years, through rapid urbanization of the country, the social & family structure is breaking down, which is contributing in development of mental health disorders amongst children. There is definite lack of infrastructure, awareness & availability of mental health services for children. However, plenty of avenues exist, through which child psychiatric services can be developed in the country.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".