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Record W4400108806 · doi:10.5195/ijt.2024.6603

The Development and Pilot Testing of a Fidelity Checklist for a Family-Centered Telehealth Intervention for Parents of Children with Motor Delay

2024· article· en· W4400108806 on OpenAlexafffund
Karen Hurtubise, Michelle Phoenix, Chantal Camden, R. Gauthier, Paul W. Stratford, Rosalie Dostie, Audrée Jeanne Beaudoin, Désirée B. Maltais, Jade Berbari, Isabelle Gaboury

Bibliographic record

VenueInternational Journal of Telerehabilitation · 2024
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsUniversité LavalMcMaster UniversityCentre for Interdisciplinary Research in RehabilitationCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
FundersChildren's Health FoundationUniversité de Sherbrooke
KeywordsChecklistTelehealthFidelityIntervention (counseling)High fidelityComputer scienceTest (biology)PsychologyMultimediaTelemedicineEngineeringTelecommunicationsHealth carePsychiatryCognitive psychology

Abstract

fetched live from OpenAlex

This multi-methods study describes the development of a pediatric rehabilitation telehealth intervention fidelity checklist, estimates its inter-rater reliability, and documents raters' implementation experience. A literature scan and expert consultation identified eighteen key behaviors and categorized them into three subdomains, measured using a 5-point measurement system. To estimate the checklist's inter-rater reliability, three raters scored 33 video recordings. A Shrout and Fleiss Class 1,1 intraclass correlation (ICC)) and 95% confidence intervals (CI) calculated ICCs = 0.5 (CI: 0, 0.9) for both the Therapist and the Parent-Therapists subdomains, and the Parent subdomain an ICC = 0.3 (CI: 0, 0.8). In the implementation surveys, raters reported high levels of satisfaction (100%), ease of use (84% to 88%), and confidence in their video ratings (87% to 100%). Changes in procedures and scoring were recommended. Capturing raters' implementation experiences is crucial in the early evaluation of the fidelity checklists for telehealth.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score0.266

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.071
GPT teacher head0.399
Teacher spread0.328 · 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
Published2024
Admission routes2
Has abstractyes

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