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Record W4406420652 · doi:10.5195/ijt.2024.6684

Describing Caregiver and Clinician Experiences with Pediatric Telerehabilitation Across Clinical Disciplines

2025· article· en· W4406420652 on OpenAlexaff
Meaghan Reitzel, Lori Letts, Cynthia Lennon, Jennifer Lasenby-Lessard, Monika Novak‐Pavlic, Briano Di Rezze, Michelle Phoenix

Bibliographic record

VenueInternational Journal of Telerehabilitation · 2025
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity of GuelphHolland Bloorview Kids Rehabilitation HospitalRegional Municipality of WaterlooMcMaster UniversityMcMaster University Medical Centre
Fundersnot available
KeywordsTelerehabilitationContext (archaeology)Scope (computer science)FeelingTelemedicineService (business)PsychologyMedical educationComputer scienceMedicineHealth careBusiness

Abstract

fetched live from OpenAlex

Scope: This study describes the high and low points of caregiver and clinician experiences with pediatric telerehabilitation with consideration for the sustainable adoption of pediatric telerehabilitation beyond the COVID-19 pandemic context. Methods: As part of a larger study, this project analyzed data from qualitative interviews to describe caregivers' (n = 27) and clinicians' (n = 27) experiences with pediatric telerehabilitation. Findings: Caregiver and clinician experiences with pediatric telerehabilitation are described according to four touchpoints identified: (1) child engagement in telerehabilitation; (2) perceived value of telerehabilitation services and caregiver engagement; (3) preparing the people and environment for telerehabilitation services; (4) fit of using a telerehabilitation model; and (5) providing family with choice. Discussion: Findings highlight the importance of being informed about the telerehabilitation service model, feeling prepared for telerehabilitation appointments and being responsive to families' choice. Recommendations to address these areas are discussed.

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)0.002
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 score0.201
Threshold uncertainty score0.454

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.050
GPT teacher head0.455
Teacher spread0.405 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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