PROTOCOL: The effects of empowerment‐based nutrition interventions on the nutritional status of adolescent girls in low‐ and middle‐income countries
Bibliographic record
Abstract
BACKGROUND 1.1 | The problem, condition or issue Adolescents (10 to 19 years) currently represent the largest global generation of young people in our collective history (United Nations, 2015).The regions of Africa, Asia, Latin America, and the Caribbean are the home of 1.1 billion young persons (United Nations Department of Economic and Social Affairs Population Division, 2017).In sub-Saharan Africa, people below the age of 25 make up 62% of the population, with only marginal declines predicted through 2050 (United Nations Department of Economic and Social Affairs Population Division, 2019).The working age population (25 to 64 years) in sub-Saharan Africa, Oceania, and parts of Asia, Latin America, and the Caribbean is growing faster than all other age groups (United Nations Department of Economic and Social Affairs Population Division, 2019).Ensuring the health and well-being of adolescents who will fill the ranks of the world's working age population will help to propel global economic growth and development (Patton et al., 2016).Adolescence is a period of significant physiological change that includes marked skeletal growth, increased bone mass, and fundamental neurological development (Das et al., 2017;Patton et al., 2016).Proper nutrition during adolescence is crucial for optimal growth and development and helps to prepare adolescents for adulthood.However, many adolescents face challenges in achieving optimal dietary intake, especially in low-and middle-income countries (LMICs) where the majority of adolescents reside (WHO, 2014).Iron-deficiency anemia affects 430.7 million (24%) adolescents, with 77% of adolescents living with anaemia in multiburden countries where communicable, maternal, and nutritional conditions contribute to 2,500 disability adjusted lifeyears (DALYs) or more per 100,000 adolescents (Azzopardi et al., 2019).The prevalence of anaemia is consistently higher for females than males, and is more than 50% for females in Bhutan, Yemen, India, and Burkina Faso in 2016 (Azzopardi et al., 2019).Mean BMI (body mass index) and the prevalence of obesity are also rising among children and adolescents --------------------------------------------------------------
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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.245 | 0.028 |
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".