Examining the double burden of malnutrition for preschool children and women of reproductive age in low-income and middle-income countries: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: The majority of the populations in low-income and middle-income countries (LMICs) are encountering the double burden of malnutrition (DBM): the coexistence of both undernutrition and overnutrition sequalae. With DBM being a new phenomenon in research, little is known about its aetiology, operational definitions and risk factors influencing its manifestation. The proposed scoping review is aimed at mapping literature with regard to the DBM phenomenon among preschool children and women of reproductive age in LMICs who are among the most high-risk groups to encounter DBM. METHODS: A comprehensive literature search will be conducted in the following electronic databases: MEDLINE, EMBASE, Scopus, CINAHL, LILACS and ProQuest Dissertations and Thesis Global. Additionally, searches in other government and institutional sources (WHO website and university repositories) and forward and backward citation tracking of seminal articles will also be done. Two reviewers will independently conduct title and abstract screening and full-text screening. Similarly, data extraction and coding will independently be done by two reviewers. Information extracted from included literature will be analysed qualitatively using thematic analysis approach and reported as per the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines. ETHICS AND DISSEMINATION: Ethical approval is not required for this study because the review is based on literature from publicly available sources. The dissemination of our findings will be done through presentations in relevant conferences and publication in a peer-reviewed journal.
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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 | 0.121 | 0.091 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.020 | 0.012 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.042 | 0.012 |
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".