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Record W2746924423 · doi:10.1044/persp2.sig9.25

Examination of Coaching Behaviors Used by Providers When Delivering Early Intervention via Telehealth to Families of Children Who Are Deaf or Hard of Hearing

2017· article· en· W2746924423 on OpenAlexaff
Arlene Stredler-Brown

Bibliographic record

VenuePerspectives of the ASHA Special Interest Groups · 2017
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTelehealthCoachingIntervention (counseling)PsychologyTelemedicineMedicineHealth careNursingFamily medicinePsychotherapist

Abstract

fetched live from OpenAlex

The Individuals with Disabilities Education Act (IDEA, 2004) states that infants and toddlers with disabilities, and their family members, are to receive family-centered early intervention (FCEI). This study investigated providers' use of FCEI strategies when intervention was delivered to young children who were deaf or hard of hearing via telehealth. Telehealth is the use of telecommunication technologies to provide health services to people who are located at some distance from a provider. Telehealth also offers access to specialists and eliminates barriers of geography and weather. This study examined the frequency of occurrence of desired FCEI provider behaviors during telehealth sessions and contrasted them with the same behaviors used during in-person therapy. The use of FCEI provider behaviors was measured by observing and coding digitally recorded intervention sessions. Results demonstrated that selected FCEI provider behaviors occur in the telehealth condition more frequently than in the in-person condition reported in the literature. Three of the provider behaviors studied (i.e., observation, parent practice with feedback, and child behavior with provider feedback) were used more frequently in the telehealth condition. Direct instruction was used in similar amounts in both treatment conditions. This study affirms that the use of FCEI strategies may be enhanced through 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)0.001
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 score0.085
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.063
GPT teacher head0.359
Teacher spread0.296 · 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

Citations19
Published2017
Admission routes1
Has abstractyes

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