Examining lifestyle behaviours and weight status of primary schoolchildren: using Mozambique to explore the data gaps in low- and middle-income countries
Bibliographic record
Abstract
The emergency of malnutrition and physical inactivity among children as serious public health challenges in low- and middle-income countries (LMICs) is concerning and requires urgent attention. The main objective of this dissertation was to examine relationships between lifestyle behaviours and weight status among schoolchildren in Mozambique and use findings to highlight important data gaps that exist in LMICs. Narrative literature searches conducted identified data gaps and research needs. A published protocol was used for this dissertation (n = 683) to facilitate data comparability. Anthropometric and accelerometry data were objectively measured while data about lifestyle behaviours and environmental factors were collected using context-adapted questionnaires. As part of this dissertation, 6 manuscripts were developed and submitted for publication in peer-reviewed scientific journals. Results showed a dearth of information and that overweight/obesity is an emerging public health concern, especially among urban children. Moderate- to vigorous-intensity physical activity (MVPA), active transport, and maternal body mass index (BMI) were important modifiable correlates of weight status for Mozambican children. Distinct differences in prevalences of lifestyle behaviours were observed between urban and rural children in Mozambique. Compared with children from 12 other countries, children from Mozambique had lower BMI, higher daily MVPA, lower daily sedentary time, and comparable sleep duration. Linear distributions of study site-specific BMI, minutes of daily MVPA, and daily sedentary time by country human development index were observed. Findings revealed important differences between urban and rural children, supporting the need to include both in study samples and especially in LMICs where most people live in rural areas.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".