Demographic and Epidemiological Transitions in Nepal: Developmental Implications
Bibliographic record
Abstract
Nepal is a country in South Central Asia that is landlocked between China and India and is rich of history and tradition.According to the Population Reference Bureau (2013), Nepal's mid-2013 population was 26.8 million, with approximately 17 per cent of the population residing in urban areas.Nepal is world-famous for Mount Everest, which is at 8,850 m (the highest point in Asia) and the prayer flags that adorn the streets in Nepal's capital city Kathmandu.In nine chapters, the book Demographic and Epidemiological Transitions in Nepal goes beyond the familiar and provides the reader detailed insight of the country with respect to the country-specific demographics and health conditions.The first chapter covers the rationale and scope of the research, focusing on how modernization factors, demographic components, and epidemiological changes interrelate in the country of Nepal.This chapter includes brief discussions of significant theoretical traditions, citing Omran (1971), Caldwell (1998), and Olshansky and Ault (1986).A conceptual framework linking modernization and demographic factors to indicators of demographic and epidemiological transition is presented in figure 1.1, which clearly demonstrates how modernization factors tend to be highly correlated.This figure not only provides a justification for discussing these components separately in different chapters of the book, but also serves as an outline of important variables in empirical analysis.The demographic transition presented in chapter 2 is a foundational theory describing shifts in human populations.The book focuses on the demographic situation in Nepal such as: pre-and post-Second World War situations and fertility transitions.By addressing population and mortality trends, such as life expectancy at birth, child mortality, fertility trends, age-specific fertility rates, and total fertility rates, Nepal's historical situation is clearly outlined.Chapter 3 focuses on the epidemiologic transition.By outlining the transition stages more broadly, the global epidemiological situation allows the author to place Nepal's epidemiological situation in context.The author notes that the "epidemiological situation in Nepal was not known until fairly recently."Nepal's history is indeed unique, yet it has entered the third stage in the epidemiologic transition.Due in part to the health and development conditions in the country, the discussion is more inclusive than purely economic.By focusing on birth intervals in Nepal, in chapter 4 the author highlights factors affecting birth intervals, such as women's education, age at marriage, traditional norm and cultural practices, and place of residence.By using the Nepal Fertility, Family Planning and Health Survey (1991), the author could choose appropriate measures and recognize the limitations of the data.The findings allow the author to outline reasons why couples have longer first birth intervals than other intervals.The author notes that women in Nepal seem to have comparatively more control over their reproductive lives.Providing women economic opportunities contributes to reductions of high fertility levels.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".