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Record W4240559618 · doi:10.1300/j006v27n02_03

Measurement Practices in Pediatric Rehabilitation

2007· article· en· W4240559618 on OpenAlexaffabout
Steven R. Hanna, Dianne Russell, Doreen J. Bartlett, Marilyn K. Kertoy, Peter Rosenbaum, Kerry Wynn

Bibliographic record

VenuePhysical & Occupational Therapy In Pediatrics · 2007
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsGlenrose Rehabilitation HospitalWestern UniversityMcMaster University
Fundersnot available
KeywordsRehabilitationPhysical medicine and rehabilitationOccupational therapyPhysical therapyMedicinePsychology

Abstract

fetched live from OpenAlex

We investigated measurement practices in pediatric rehabilitation. We conducted a survey of 63 physical, 72 occupational, and 74 speech-language therapists working in one of 16 children's rehabilitation programs in Ontario, Canada. Therapists were surveyed about their measurement practices, and their confidence, beliefs, and attitudes about measurement. Results showed that standardized clinical measures were used frequently, but were often modified. Clinicians rated themselves as least comfortable with statistical concepts related to the uncertainty in test scores, and rated factors related to finding appropriate measures as the most important influences on their measurement practices. Some variance in measurement attitudes and practices was associated with treatment centre of practice, suggesting that there may be organizational or peer influences on measurement behaviour. The results have implications for continuing education, measurement development, and interventions designed to facilitate sound measurement practices.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.077
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.067
GPT teacher head0.378
Teacher spread0.311 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations30
Published2007
Admission routes2
Has abstractyes

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