Posture analysis of lifting a load for head carriage and comparison between pregnant and non-pregnant women
Bibliographic record
Abstract
BACKGROUND: In Western Africa, women continue performing heavy physical work that includes carrying loads on their heads during pregnancy. Women may adapt to pregnancy related body changes by modifying their postures to perform such tasks. OBJECTIVE: The objectives of this biomechanical task analysis study were to 1) determine sagittal plane postures of the trunk and upper extremities at specific events during the task of lifting and lowering a load to be carried on the head, 2) compare postures of pregnant and non-pregnant participants, 3) evaluate risk for musculo-skeletal disorders (MSD) with the rapid entire body assessment (REBA) criteria. PARTICIPANTS: Twenty-six pregnant (26 ± 5 years, 159 ± 9 cm, 63 ± 15 kg, 25 ± 9 weeks of pregnancy) and 25 paired non-pregnant retail merchants were recruited in Porto-Novo (Benin). METHODS: Participants were recorded on video in a laboratory setting while they lifted a tray (20% body weight) from a stool to their head and then put it back down. Trunk inclination and knee, shoulder and elbow flexion angles were determined using Dartfish® software. RESULTS: The trunk was bent by more than 80° at pick-up and set-down and knees were moderately flexed, significantly less (< 11°) for pregnant women, possibly because it was harder to lift the trunk, or for stability. For all postures analysed, the majority of trials were classified as "high" risk or "very high risk" for MSD. CONCLUSIONS: Future research should investigate prevalence of MSDs in this population to confirm the results of this study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".