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Record W2803177353 · doi:10.1111/dmcn.13906

‘Power in Mobility’: parent and therapist perspectives of the experiences of children learning to use powered mobility

2018· article· en· W2803177353 on OpenAlexaff
Lisa K. Kenyon, W. Ben Mortenson, William C. Miller

Bibliographic record

VenueDevelopmental Medicine & Child Neurology · 2018
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyPower (physics)Process (computing)Developmental psychologyComputer science

Abstract

fetched live from OpenAlex

AIM: The aim of this study was to gain insights, from the perspectives of both parents and pediatric therapists, into the experiences of children learning to use a power mobility device. METHOD: The purposive sample included 33 participants: 14 parents of children who were learning, or had learned, to use a power mobility device and 19 pediatric occupational therapists or physical therapists. Data were gathered face-to-face via seven focus groups consisting of either parents or therapists, and eight one-on-one interviews. Data were analyzed using the constant comparative method. RESULTS: Three main themes were identified: (1) 'Power in mobility' described how learning to use powered mobility changed more than just a child's locomotor abilities; (2) 'There is no recipe' revealed how learning to use powered mobility occurred along an individualized continuum of skills that often unfolded over time in a cyclical process; (3) 'Emotional journey' explored how learning to use powered mobility was an emotionally charged undertaking for all those involved. INTERPRETATION: Learning to use a power mobility device is a complex process that often requires perseverance and determination on the part of the child, family, and therapist. WHAT THIS PAPER ADDS: Powered mobility use impacts more than just a child's locomotor abilities. Learning to use a power mobility device is a highly individualized process. Learning to use powered mobility may be an emotionally charged process.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.012
GPT teacher head0.260
Teacher spread0.249 · 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".

Quick stats

Citations28
Published2018
Admission routes1
Has abstractyes

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