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Record W2588069792 · doi:10.3233/wor-172481

Associations between trunk flexion and physical activity of patient care workers for a single shift: A pilot study

2017· article· en· W2588069792 on OpenAlexaff
Oscar E. Arias, Peter Umukoro, Sonja D. Stoffel, Karen Hopcia, Glorian Sorensen, Jack T. Dennerlein

Bibliographic record

VenueWork · 2017
Typearticle
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsCamosun College
FundersNational Institute for Occupational Safety and Health
KeywordsTrunkPhysical activityMedicinePhysical therapyInclinometerPhysical medicine and rehabilitationWork (physics)AccelerometerPositive correlationComputer scienceEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: Trunk flexion and occupational physical activity are parameters that have been used to assess and characterize jobs with high physical demands. OBJECTIVE: Characterize the physical load of trunk flexion and physical activity of patient care unit (PCU) workers during a single work shift. METHODS: Participants wore an accelerometer to measure physical activity and an inclinometer to assess trunk flexion during a single work shift, which was compared using correlation and linear regression analyses. RESULTS: Participants spent 74% of their work time upright between - 10° to 20° and 19% of their time flexed between 20° to 45°. On average workers spent 3% and 5% of their time, in the extreme postures of less than - 10° and greater than 45°, respectively. Participants spent 99% of their shift below moderate and vigorous activity. The largest correlation found was between the number of forward trunk flexions to 20° degrees per shift and minutes in lifestyle activity (r = 0.6, p < 0.001). No correlations between minutes of moderate or vigorous physical activity and trunk flexion were observed. CONCLUSIONS: This study suggests that the physical demands of patient care unit workers as measured through trunk flexion are associated with lifestyle and light levels of physical activity.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.157
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

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

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 head0.068
GPT teacher head0.352
Teacher spread0.284 · 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 teacher head, 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

Citations15
Published2017
Admission routes1
Has abstractyes

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