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Record W2789034717

Exploring the Utility of Inter-Segmental Coordination to Assess Movement Competency During Lifting Tasks

2018· dissertation· en· W2789034717 on OpenAlexaboutno aff
Claragh E. E. Pegg

Bibliographic record

VenueUWSpace (University of Waterloo) · 2018
Typedissertation
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsnot available
Fundersnot available
KeywordsMovement (music)PsychologyCognitive psychologyPhysical medicine and rehabilitationMedicineArtAesthetics
DOInot available

Abstract

fetched live from OpenAlex

Pre-employment screens are used within the hiring process to determine the hiring or placement of employees in the workplace. It is important that such screens adequately replicate or generalize to the work of interest. The objective of this study was to determine if individuals move similarly in the Epic Lift Capacity (ELC) test, a common pre-employment screen, compared to how they move when lifting during a long-duration work simulation, where movement was characterized as inter-segmental coordination. Twenty participants (7 males, 13 females) performed the ELC test, which uses a psychophysical approach to determine a participant’s perceived maximum lift capacity, proceeded by a 90-minute work simulation. Using motion capture data lumbopelvic, hip, and knee Relative Phase Angles (RPAs) were calculated using trunk and lower limb segment angles and velocities. The Mean Absolute Relative Phase (MARP) was calculated to quantify the overall coordination pattern of each joint in each trial, while the deviation phase (DP) was calculated to quantify variability in joint coordination within a trial. Measures of coordination were calculated and averaged over the first three lifts (initial lifts) and last three lifts (final lifts) of the 90-minute work simulation and were compared to the coordination measures associated with the lifts in the ELC test. Height (floor-shoulder, floor-knuckle, and knuckle-shoulder) and load (4.54 kg, and 75% of a participant’s maximum) were controlled across conditions. Results from this study show that when considering coordination broadly across all joints, coordination was most in-phase and least variable at the lumbopelvic joint relative to the more distal joints. Also, no differences were found between the ELC test and work trials at the lumbopelvic joint, suggesting that movement, at least about the lumbopelvic joint, was controlled similarly in the ELC test and simulated work trials. Considering the high incidence rate of lower back injuries in the workplace (Statistics Canada, 2014), investigation into the stability and coordination of movements at the lumbopelvic joint is of interest, and it is reassuring the lumbopelvic motion is similarly controlled in work as it is when performing the ELC. In contrast, at the hip and knee the coordination patterns were generally less in-phase (higher MARP) and showed more variability (higher DP) during the ELC test compared to both the initial and final lifts; however, differences at the knee appeared to be modulated by both height and load. In contrast, lumbopelvic joint coordination only changed between the initial and final lifts within the 90-minute simulation, as the final lifts were less in-phase than in the initial lifts. The coordinative changes seen in this study may reflect functional organismic and task constraint differences between the tasks. Functional organismic constraints, such as fatigue or boredom may have resulted in coordinative changes over time in the work simulation, whereas task changes, such as task goal or objective, may have resulted in coordinative changes in the ELC test compared to the work simulation. Due to these apparent coordinative differences with changes in the participant and task, movement may be influenced by psychological, physical, and environmental factors, acting as constraints by altering movement outcomes (Glazier, 2017; Newell, 1986). These constraints may be important to incorporate into the future design and use of pre-employment screens when movement strategy is of importance, as changes in coordination did occur in this study, with small changes in objectives or over time, despite identical structural environmental design.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.058
GPT teacher head0.239
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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