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Record W4322775267 · doi:10.3390/children10030492

Tele-Coaching Korean Parents for Improving Occupational Performance of Toddlers: Three Case Reports

2023· article· en· W4322775267 on OpenAlexaboutno aff
Dabin Choi, Aeri Yu, Misun Kim, Eun Young Kim

Bibliographic record

VenueChildren · 2023
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsnot available
FundersKorea Institute for Advancement of TechnologyMinistry of Trade, Industry and EnergySoonchunhyang University
KeywordsCoachingOccupational therapyCompetence (human resources)TelehealthPsychologyIntervention (counseling)Clinical psychologyMedicineTelemedicinePsychiatryHealth careSocial psychologyPsychotherapist

Abstract

fetched live from OpenAlex

Telehealth has been applied to occupational therapy practice since the COVID-19 pandemic, but no research has been conducted on the use of telehealth to improve the occupational performances of Korean children and parents. This study explored the possibility of tele-coaching parents to improve toddlers' occupational performance and parenting competence in Korea. Three mothers of toddlers received Occupational Performance Coaching (OPC) via videoconference. The Canadian Occupational Performance Measure (COPM) and the Parenting Sense of Competence Scale (PSOC) were used pre- and post-intervention to measure the occupational performances of the toddlers and parents and parenting competence. Post-intervention interviews were conducted to explore the parents' experiences with the tele-coaching and analyzed by content analysis. Most of the COPM scores showed a significant increase. The PSOC scores also increased. The mothers reported their learning, the changes in their children, the appropriateness of the coaching, and the usefulness of the tele-coaching delivery. The findings demonstrate the potential of tele-coaching as a practical intervention for Korean children and parents.

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)0.000
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 score0.022
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.062
GPT teacher head0.359
Teacher spread0.297 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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