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Record W3173934235 · doi:10.5195/ijt.2021.6363

Early Intervention Service Delivery via Telehealth During COVID-19: A Research-Practice Partnership

2021· article· en· W3173934235 on OpenAlexaboutno aff
Jessica Kronberg, Elaine Tierney, Anna Wallisch, Lauren Little

Bibliographic record

VenueInternational Journal of Telerehabilitation · 2021
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsnot available
Fundersnot available
KeywordsTelehealthCoachingIntervention (counseling)General partnershipGoal Attainment ScalingService providerPsychologyService delivery frameworkNursingMedical educationService (business)TelemedicineMedicineFamily medicineHealth careBusinessPolitical science

Abstract

fetched live from OpenAlex

Coaching has been identified as a best practice for early intervention (EI) services provided through the Individuals with Disabilities Education Act (IDEA) Part C. The current study describes the establishment and progress of a research-relationship partnership to deliver coaching via telehealth during the COVID-19 pandemic. Community-based EI providers implemented 9-weeks of telehealth coaching and evaluated the extent to which child and caregiver outcomes differed between families that had previously received in-person services versus telehealth only. Four EI providers completed the intervention with n=17 families of children aged 6-34 months during the pandemic (April-August 2020). We used the Canadian Occupational Performance Measure (COPM) and Goal Attainment Scaling (GAS) to collect outcomes on caregiver identified goals; we used Wilcoxon Signed Rank Tests to examine pre- to post-intervention data. Results showed significant improvements in parent satisfaction, child performance, and goal attainment (all p<.01). Findings suggest that telehealth coaching procedures implemented by community-based EI providers resulted in improvements in caregiver identified goals for young children.

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)0.006
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.306
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.134
GPT teacher head0.496
Teacher spread0.362 · 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.

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

Citations32
Published2021
Admission routes1
Has abstractyes

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