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Record W6927113840 · doi:10.26077/ff20-5e9d

“It gives me confidence”: Caregiver Coaching From the Perspective of Families of Children Who Are Deaf or Hard of Hearing

2022· article· en· W6927113840 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Commons - USU (Utah State University) · 2022
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsnot available
Fundersnot available
KeywordsCoachingActive listeningPerspective (graphical)Intervention (counseling)Context (archaeology)Qualitative researchPsychological intervention

Abstract

fetched live from OpenAlex

Caregiver coaching is utilized in early intervention services with families of children who are deaf or hard of hearing to increase caregivers’ skills and confidence in supporting their child’s language development, but few studies have examined coaching from the perspective of the caregivers. The purpose of this study was to increase understanding of caregivers’ experiences of coaching in the context of listening and spoken language intervention services. Using semi-structured, qualitative interviews, this study examined 13 caregivers’ perspectives at three intervention sites in the US and Canada. Results indicate that caregivers perceive that practitioner characteristics, expectations, and the evolution of the coaching relationship over time contribute to a positive caregiver coaching relationship. This study contributes to the understanding of the caregiver coaching experience and has implications for new and experienced practitioners working to improve their practice by establishing and strengthening collaborative caregiver coaching relationships with the families they serve.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.007
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.283
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2022
Admission routes1
Has abstractyes

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