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Record W2763416674 · doi:10.1177/0308022617726730

The Effect of Pain Scale for functional capacity evaluations

2017· article· en· W2763416674 on OpenAlexaff
Shelly Dornian, Joel A Short, Shannon I Smith, Lindsey A Townsend, Sara Morassaei, Susan Forwell

Bibliographic record

VenueBritish Journal of Occupational Therapy · 2017
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of British ColumbiaSurrey Memorial HospitalFraser Health
Fundersnot available
KeywordsContent validityReliability (semiconductor)CredibilityScale (ratio)Physical therapyMedicinePhysical medicine and rehabilitationPsychologyClinical psychologyPsychometrics

Abstract

fetched live from OpenAlex

Introduction The Effect of Pain Scale is a new tool to evaluate the effect of pain on function during functional capacity evaluations. The aim was to test the clinicometric properties of the scale with clinicians familiar with the tool and workers with musculoskeletal injuries. Method The study was conducted in two stages. Stage 1 assessed clinical utility and content validity of the scale using a questionnaire for clinicians using the tool during functional capacity evaluations. In stage 2, data were collected from clients and clinicians during functional capacity evaluations and were used to assess criterion validity, inter-rater reliability, and responsiveness. Results Twelve clinicians responded to the survey on clinical utility and content validity, and data were recorded from 30 clients during their functional capacity evaluations. The tool demonstrated good clinical utility, content validity, inter-rater reliability, and criterion validity, and was responsive to the effects of pain on function as rated by both clients and clinicians. Conclusion Findings show its credibility as a tool with sound clinicometrics and establish its value for functional capacity evaluations with clients who have musculoskeletal injuries. Further testing in other clinical settings and client populations is needed to further establish the clinical value of this tool.

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)0.005
metaresearch head score (Gemma)0.003
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 score0.709
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.058
GPT teacher head0.380
Teacher spread0.322 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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