THE EFFECT OF LOAD PLACEMENT ON STATIC POSTURE AND REACTION FORCES IN YOUTH
Bibliographic record
Abstract
The ramifications of weight-bearing induced stress imposed by schoolbags is a serious issue when considering youth who are experiencing physical growth and motor development. Determining load limits and proper positioning of the backpack for children may help alleviate pain and injury. PURPOSE To investigate the effects of different load placements on static posture in youth, specifically upper body and neck position in relation to lumbar and shoulder reaction forces. METHOD Sixteen children, mean (SD) age and weight 10.6 (.5) years and 49.6 (9.7) kg, performed three static trials bearing a 6.8 kg backpack in three conditions: no pack, as well as adjacent to C7 (high), and T7 (low). Static pictures were digitized to determine head lean, upper body lean, and shoulder and lumbar reaction forces. One-way ANOVA with repeated measures were used to determine differences between the three conditions (α < .05). RESULTS Head lean increased significantly, while total body lean did not significantly alter with the low load position. Lumbar reaction forces were 108.7 N for the high backpack position and 72.2 N in the low condition. Shoulder reaction forces were 118.1 N and 92.5 N for the high and low conditions, respectively. Both lumbar and shoulder reaction forces were significantly different, as were the total ratio forces (lumbar +shoulder)/load) of 3.4 and 2.5 for the high and low backpack positions. CONCLUSION The amount of relative weight youth carry was magnified in the high load condition, while posture was sacrificed in the low load condition.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".