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Record W2132294932 · doi:10.1080/17483100802715084

Evaluation of a parent-report diary of the home use of assistive devices by young children with cerebral palsy

2009· article· en· W2132294932 on OpenAlexaff
Stephen E. Ryan, Kristy A. Campbell

Bibliographic record

VenueDisability and Rehabilitation Assistive Technology · 2009
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsUniversity of TorontoToronto Rehabilitation InstituteHolland Bloorview Kids Rehabilitation Hospital
Fundersnot available
KeywordsCerebral palsyPhysical medicine and rehabilitationMedicinePsychologyPediatricsDevelopmental psychologyPhysical therapy

Abstract

fetched live from OpenAlex

PURPOSE: To develop and evaluate the preliminary measurement properties of a parent-report diary of the home use of seating and mobility devices by young children with cerebral palsy (CP). METHOD: Four AT experts reviewed the home use of technology for children (HUTCH) diary to confirm its coverage of AT devices, and six parents of young children with CP examined its content, wording and organization. A random sample of 12 other parents independently completed a HUTCH diary daily for 1 week to record their child's use of seating, mobility and orthotic devices at home. Two to three weeks later, parents completed a second diary of AT device use over another seven consecutive days. RESULTS: The face validity, content validity and test-retest reliability (ICC = 0.91; 95% CI = 0.69-0.97) of the HUTCH were very good. Parents reported that they completed the diary quickly and easily. CONCLUSIONS: The HUTCH diary shows promise as a reliable and practical way to record the frequency and number of hours that children use different types of seating and mobility-related devices at home. Testing the concurrent validity of the HUTCH diary against an acceptable criterion measure will improve its acceptance as measure of AT device use.

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)0.004
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.005
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.042
GPT teacher head0.384
Teacher spread0.341 · 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.

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

Citations10
Published2009
Admission routes1
Has abstractyes

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