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Record W2900336468 · doi:10.1080/07317107.2018.1506661

Effects of a Center-Based Parent Training Package on Parents’ Accuracy of Generalized Program Implementations at Home

2018· article· en· W2900336468 on OpenAlexaff
Lynn Yuan, Gabrielle T. Lee, Barbara Kimmel

Bibliographic record

VenueChild & Family Behavior Therapy · 2018
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsWestern University
Fundersnot available
KeywordsImplementationCoachingPsychologyMultiple baseline designParent trainingFidelityMedical educationComputer scienceIntervention (counseling)Medicine

Abstract

fetched live from OpenAlex

The purpose of this study was to evaluate the effects of a parent training package on parents’ accuracy of program implementations and their children’s goal achievements on parent-implemented programs at home. Parents also completed a quiz with questions about three-contingencies before and after the training. Three mother-child dyads participated in this study. All three children were 4-year-old boys with developmental delays. We employed a multiple baseline across three parent-child dyads as the primary design combined with a pretest and posttest. The individual parent training sessions consisted of office meetings and in-vivo classroom coaching sessions on program implementations. Each parent was trained individually to mastery criteria on program implementations using the Teacher Performance Rate Accuracy Scale (TPRA). After completing the parent training package, all parents acquired program implementations skills, and their program implementation skills were generalized to teach new behaviors at home with a high level of fidelity. Their quiz scores on three-term contingencies also increased to a relatively high level.

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)0.003
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.023
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 science0.0010.001
Research integrity0.0000.001
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 head0.118
GPT teacher head0.428
Teacher spread0.310 · 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 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

Citations7
Published2018
Admission routes1
Has abstractyes

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