MétaCan
Menu
Back to cohort
Record W3201850215 · doi:10.1080/10400435.2021.1974980

Face validity of standardized assessments for wheeled mobility & seating evaluations

2021· article· en· W3201850215 on OpenAlexaboutno aff
Bethany Semancik, Mark R. Schmeler, Richard M. Schein, Rachel Hibbs

Bibliographic record

VenueAssistive Technology · 2021
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsnot available
FundersNational Institute on Disability, Independent Living, and Rehabilitation Research
KeywordsStandardizationFace validityPollingWheelchairReliability (semiconductor)Content validityStandardized testApplied psychologyPsychologyComputer scienceMedical educationMedicinePsychometricsClinical psychologyWorld Wide Web

Abstract

fetched live from OpenAlex

A problem in the Complex Rehabilitation Technology industry is the lack of standardization in the assessment for wheeled mobility and seating (WMS). The aim of this paper was to identify assessment tools commonly used by clinicians during WMS evaluations. After the tools were identified by a panel of 12 subject matter experts, a presentation at the 2018 International Seating Symposium in Vancouver, Canada and the 2018 European Seating Symposium in Dublin, Ireland polled attendees via the Sli.do polling application to determine professional opinions of each tool, resulting in face validity for use in wheelchair evaluations. The Lawshe Content Validity Ratio was used to convert this anecdotal data into numerical data, indicating which tools were most and least used by attendees. Finally, a literature search was conducted to determine the reliability, validity, and International Classification of Functioning, Disability, & Health domain for each measure. The findings indicate that while there are many standardized and reliable assessment tools available for wheeled mobility and seating evaluations, most clinicians use only a few standardized assessment tools during WMS evaluations.

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)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.163
GPT teacher head0.517
Teacher spread0.355 · 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 designBench or experimental
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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueAssistive TechnologySame topicSpinal Cord Injury ResearchFrench-language works237,207