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Record W4317644411 · doi:10.1177/216507990605400105

Musculoskeletal Injuries among Ultrasound Sonographers in Rural Manitoba

2006· article· en· W4317644411 on OpenAlexaboutno aff
Margaret Friesen, Rebecca Friesen, Arthur O. Quanbury, Suzanne Arpin

Bibliographic record

VenueAAOHN Journal · 2006
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsnot available
Fundersnot available
KeywordsHuman factors and ergonomicsMedicineOccupational safety and healthSonographerRural areaWork (physics)SoftwareMedical educationPoison controlMedical emergencyEngineeringUltrasonographyComputer scienceSurgeryPathology

Abstract

fetched live from OpenAlex

The purpose of this study was to compare the epidemiology of musculoskeletal injuries and workplace ergonomics among rural-based sonographers compared to urban-based sonographers. The authors also tested the use of a biomechanical software program to assess load on the spine and upper extremity joints. A mail-in survey was sent to all practicing sonographers in rural Manitoba, and on-site video-taped ergonomic and biomechanical evaluations were completed for four sites. Rural-based sonographers appear to have greater work stress related to waiting lists, use of outdated and non-adjustable equipment, and high client load. They also do not advocate for better working conditions because they are few in number and geographically distant from one another. Use of the biomechanical software proved minimally useful. Information related to industry standards and greater collaboration is needed to promote workplace health for sonographers.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.813
Threshold uncertainty score0.373

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.409
Teacher spread0.385 · 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

Citations25
Published2006
Admission routes1
Has abstractyes

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