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Record W4402532383 · doi:10.1002/jaba.2912

On the efficacy and efficiency of treating pediatric feeding disorder

2024· review· en· W4402532383 on OpenAlexaff
Victoria Scott, Valdeep Saini, Micaela Totino

Bibliographic record

VenueJournal of Applied Behavior Analysis · 2024
Typereview
Languageen
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsBrock University
Fundersnot available
KeywordsPsychological interventionExtinction (optical mineralogy)PsychologyIntervention (counseling)Meta-analysisDevelopmental psychologyClinical psychologyMedicinePsychiatryBiologyInternal medicine

Abstract

fetched live from OpenAlex

Inappropriate mealtime behavior (IMB) is a type of feeding challenge within the broader class of food refusal. The purpose of this study was to critically analyze the efficacy of interventions for the treatment of IMB through a meta-analysis of research using single-case experimental designs. We examined the extent to which different interventions resulted in decreases in IMB while also producing increases in food acceptance. This meta-analysis was also used to examine the efficiency of different interventions in achieving clinical significance. We identified 38 studies involving 266 cases in which IMB was treated with a behavioral intervention. The results indicated interventions that combined escape extinction and non-escape extinction had greater effect sizes for both reducing IMB and increasing food acceptance than either escape extinction alone or non-escape extinction alone. However, interventions that included escape extinction were slightly less efficient at decreasing IMB than were interventions that did not include escape extinction. We discuss the implications of these findings and provide recommendations for future research.

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)0.009
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.008
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.355
Teacher spread0.318 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2024
Admission routes1
Has abstractyes

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