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Record W1661575768 · doi:10.3233/wor-2010-0993

Best practices in the rehabilitation of acute musculoskeletal disorders in workers with injuries: An integrative review and analysis of evolving trends

2010· review· en· W1661575768 on OpenAlexaff
Rosemary Lysaght, Catherine Donnelly, Dorothy Luong

Bibliographic record

VenueWork · 2010
Typereview
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsQueen's University
Fundersnot available
KeywordsRehabilitationMedicinePhysical therapyPhysical medicine and rehabilitationPsychology

Abstract

fetched live from OpenAlex

OBJECTIVE: Strategies for the restoration of function and job performance in workers with musculoskeletal disorders have changed considerably since industrial rehabilitation became a distinct specialization in the 1980s. A rich body of research concerning approaches to medical and rehabilitative management of these disorders exists, resulting in a large number of hybrid approaches worldwide. METHODS: This integrative review examined the evidence base for best practices in the rehabilitation of acute musculoskeletal workplace injuries, and also mapped the evolution of approaches over the past thirty years. Contextual factors that may have contributed to change were assessed through analysis of changing practice trends and review of descriptive literature over time. RESULTS: A clear movement away from simplistic, unidimensional approaches towards comprehensive workplace interventions is evident. Economic concerns and growing government and insurance regulation of workplace safety and injury management were the likely drivers of change. While the contributions of various elements of disability management in the workplace were examined, many features of onsite interventions remain to be examined. CONCLUSIONS: A strong body of research has produced notable advances in management of acute musculoskeletal workplace injury. Research concerning the delivery of workplace-based interventions, the role of workplace environment factors and a range of worker outcomes will further advance knowledge in this field.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.907
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.006
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.379
Teacher spread0.363 · 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 designOther design
Domainnot available
GenreReview

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

Citations11
Published2010
Admission routes1
Has abstractyes

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