The Influence of Contextual and Theoretical Expertise on Generic and Occupation-Specific Lifting Strategy
Bibliographic record
Abstract
OBJECTIVE: To determine whether (i) low back loads and/or (ii) kinematic coordination patterns differed across theoretical expert, contextual expert and novice groups when completing both generic and occupation-specific lifts. BACKGROUND: Experience has been proposed as a factor that could reduce biomechanical exposures in lifting, but the literature reports mixed effects. The inconsistent relationship between experience and exposures may be partially attributable to the broad classification of experience and experimental lifting protocols not replicating the environment where experience was gained. METHODS: Purposive sampling was used to recruit 72 participants including theoretical experts (formal training on lifting mechanics), contextual experts (paramedics), and novices. Participants performed 10 barbell and crate (generic) lifts, as well as backboard and stretcher (occupation-specific) lifts while whole-body kinematics and ground reaction forces were collected. Peak low back compression and anteroposterior shear loads normalized to body mass, as well as kinematic coordination patterns, were calculated as dependent variables. RESULTS: No significant differences in low back loads were observed across expertise groups. However, significant differences were seen in kinematic coordination patterns across expertise groups in occupation-specific lifts, but not in generic lifts. CONCLUSION: Increasing expertise is unlikely to minimize low back loads in lifting. However, contextual expertise did influence lifting kinematics, but only when performing occupationally specific lifts. APPLICATION: Contextual expertise may help lifters adopt lifting kinematics that enhance the tolerance of their musculoskeletal system to withstand applied loads, but does not seem to reduce the applied low back loads relative to noncontextual expert groups.
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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 | 0.008 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".