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Record W1695630697 · doi:10.3233/wor-2008-00694

An investigation of a workplace-based return-to-work program for shoulder injuries

2008· article· en· W1695630697 on OpenAlexaff
Lynn Shaw, Susan A. Domanski, A. Freeman, C. Hoffele

Bibliographic record

VenueWork · 2008
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of Northern British ColumbiaWestern University
Fundersnot available
KeywordsWork (physics)Context (archaeology)Occupational safety and healthMedicineHuman factors and ergonomicsNursingPhysical therapyPoison controlMedical educationMedical emergencyEngineering

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate and evaluate the current workplace management of rotator cuff injuries in a manufacturing plant. The secondary aims were to examine the impact of the company's return-to-work processes, compare outcomes to current industry standards for work (re)entry and to identify the components that characterized this workplace-based return-to-work (RTW) program. This investigation involved a case study approach comprised of an examination of the program context using interviews, onsite visits, a document review and a retrospective analysis of the RTW experiences of 184 workers with shoulder injuries. Findings revealed that the workplace-based RTW program was consistent with and shaped by the organizational culture of problem solving, knowledge exchange and equitable participation of workers, supervisors and health professionals. These components contributed to the program in achieving the following outcomes for workers with shoulder injuries. One-third of workers were placed on modified duties within three days, 56% of workers who engaged in an early RTW program returned to work within one month. Overall, 87.8% of workers with rotator cuff injuries successfully returned to pre-injury work. The implications of developing capacity for workplace-based programs to manage injuries at work are discussed.

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)0.001
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 score0.032
Threshold uncertainty score0.665

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.118
GPT teacher head0.482
Teacher spread0.364 · 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

Citations16
Published2008
Admission routes1
Has abstractyes

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