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Record W2943568774 · doi:10.1080/17518423.2019.1604580

The Nature, Value, and Experience of Engagement in Pediatric Rehabilitation: Perspectives of Youth, Caregivers, and Service Providers

2019· article· en· W2943568774 on OpenAlexafffund
Gillian King, Lisa A. Chiarello, Roger Ideishi, Rachel D’Arrigo, Eric Smart, Jenny Ziviani, Madhu Pinto

Bibliographic record

VenueDevelopmental Neurorehabilitation · 2019
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsHolland Bloorview Kids Rehabilitation HospitalToronto Rehabilitation InstituteUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsRehabilitationService providerValue (mathematics)PsychologyService (business)NursingMedical educationMedicineBusinessPhysical therapyComputer science

Abstract

fetched live from OpenAlex

Purpose: To conduct a qualitative investigation of engagement in pediatric rehabilitation therapy.Methods: Interviews were conducted with 10 youth, 10 caregivers, and 10 service providers. Transcripts were analyzed thematically using an inductive approach.Results: Themes illustrated three perspectives: engagement as a connection with components of the therapy process, engagement as working together, and engagement as an affective and motivational process. Engagement created valued connections with therapy components and forward momentum for therapy.Conclusions: The themes supported a view of engagement as complex, transactional, and multidimensional. Participants focused on different, yet not discrepant, aspects of engagement. Youth focused on having fun and personal connection with service providers. Caregivers provided a more complex perspective encompassing both their own and their child’s engagement, with an emphasis on relationship, understanding what is taking place, and feeling valued in the process. Service providers highlighted goal attainment and the value of engagement in bringing about outcomes.

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)0.001
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 score0.099
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.016
GPT teacher head0.305
Teacher spread0.288 · 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

Citations54
Published2019
Admission routes2
Has abstractyes

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