The effect of shoulder strap width and load placement on shoulder-backpack interface pressure
Bibliographic record
Abstract
BACKGROUND: Pressure on the shoulder can be a major limiting factor to backpack use and poor design can lead to pain and injury. OBJECTIVE: To evaluate the effect of shoulder strap width and load placement in a backpack on the shoulder and axilla. METHODS: A manikin fitted with a backpack load of 20 kg mass and four different width straps (5, 6, 7, and 8 cm) was used. The load was placed high or low. Interface pressure sensors were placed over the shoulder and chest wall at the axilla. RESULTS: A significant interaction was observed between shoulder strap width and load placement. The positive effect of wide straps on shoulder pressure is greater with high load placement and the benefit of wide straps on axillary pressure is improved with low load placement. Interface pressure decreased significantly from narrow to wide straps. A large difference was noted between interface pressure on high and low load placement with narrow straps; however, as shoulder strap width increased, the difference between the two load placements decreased. CONCLUSION: The least amount of interface pressure was observed with 8 cm shoulder straps and high load placement. These findings should influence design and use of backpacks.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".