Lifestyle transitions and the developing world: Reflections on the implications for health, well-being and wealth
Bibliographic record
Abstract
Context: The past few decades witnessed significant economic growth in many developing countries of the world. These economic changes towards increasing gross domestic product (GDP) brought with it several other transitions in these countries: demographic, epidemiological, technological, and nutritional. These resulted in improving the living standards as well as life expectancy in many of these countries. However, of public health concern is the fact that these transitions paradoxically have their negative consequences on the health, well-being and wealth of the populace in these countries. Objectives: This review therefore assesses the evidence of the extent to which these changes have affected the living patterns in many developing countries and the epidemiological implications besides others issues on the populace in these countries. Methods: By using key words, the author involved a broad search of literatures on lifestyle changes, economic growth, nutrition, urbanization, smoking and alcohol, communicable and non-communicable diseases in countries termed low and middle income. Findings and conclusion: The review identified discernible evidence base about the implications of these changes on health, well-being and wealth of these nations. Accordingly, as lifestyle transitions now come to bear, it thus necessitates an all inclusive approach that will include proactive and pre-emptive interventions as well as consistent participation from governments, multilateral institutions, research-funding agencies, donors, and other players in health systems. This is because it will provide the global community with great opportunities in uniting high, middle, and low-income countries in a common purpose, given the shared interests of globalization and economic burdens worldwide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".