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Record W1989164091 · doi:10.5271/sjweh.746

Methodological issues in evaluating workplace interventions to reduce work-related musculoskeletal disorders through mechanical exposure reduction

2003· article· en· W1989164091 on OpenAlexafffund
Donald C. Cole, Richard Wells, Mardy B. Frazer, Mickey Kerr, Patrick Neumann, Andrew C. Laing

Bibliographic record

VenueScandinavian Journal of Work Environment & Health · 2003
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of WaterlooWestern UniversityInstitute for Work & Health
FundersWorkplace Safety and Insurance Board
KeywordsPsychological interventionReduction (mathematics)Work (physics)Intervention (counseling)Set (abstract data type)Work-related musculoskeletal disordersMusculoskeletal disorderApplied psychologyHuman factors and ergonomicsPsychologyRisk analysis (engineering)Computer scienceMedicinePoison controlEngineeringEnvironmental healthNursing

Abstract

fetched live from OpenAlex

Researchers of work-related musculoskeletal disorders are increasingly asked about the evidentiary base for mechanical exposure reductions. Mixed messages can arise from the different disciplinary cultures of evidence, and these mixed messages make different sets of findings incommensurate. Interventions also operate at different levels within workplaces and result in different intensities of mechanical exposure reduction. Heterogeneity in reporting intervention processes and in measuring relevant outcomes makes the synthesis of research reports difficult. As a means of synthesizing the current understanding of measures, this paper describes a set of intervention and observation nodes for which relevant workplace indicators prior to, during, and after mechanical exposure reduction can provide useful information. On the basis of this path of impacts from exposure reduction, an approach to the evaluation of multilevel ergonomic interventions is described that can assist fellow researchers in producing evidence relevant to the challenges faced by workplace parties and policy makers.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.079
GPT teacher head0.415
Teacher spread0.337 · 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

Citations61
Published2003
Admission routes2
Has abstractyes

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