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Record W4205680122 · doi:10.1002/jaba.901

On the role of operant contingencies in the maintenance of inappropriate mealtime behavior: An epidemiological analysis

2021· article· en· W4205680122 on OpenAlexaff
Valdeep Saini, Ashley S. Andersen, Joshua Jessel, Hanna Vance

Bibliographic record

VenueJournal of Applied Behavior Analysis · 2021
Typearticle
Languageen
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsBrock University
Fundersnot available
KeywordsReinforcementGeneralityPsychologyFunctional analysisDevelopmental psychologyClinical psychologyEpidemiologyToken economyApplied behavior analysisPsychotherapistMedicineSocial psychologyAutism

Abstract

fetched live from OpenAlex

Functional analysis is the primary assessment used to determine the function of inappropriate mealtime behavior in children with feeding disorders. Based on single-case experimental design studies and recent reviews, the prevalence of negative reinforcement alone in the maintenance of inappropriate mealtime behavior appears to be much greater than positive reinforcement alone. We conducted a retrospective consecutive-controlled case series to determine the generality of previous findings. Results of the epidemiological analysis support prior research in that negative reinforcement in the form of escape (44.8%), and multiple control (i.e., positive and negative reinforcement) in the forms of escape and attention (37.2%), are considerably more prevalent than positive reinforcement alone (2.5%). We discuss the relationship between functional analysis of inappropriate mealtime behavior and treatment utility. Further, we describe avenues of future research designed to advance the application of functional analysis in feeding disorders beyond inappropriate mealtime behavior.

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)0.012
metaresearch head score (Gemma)0.029
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.034
GPT teacher head0.314
Teacher spread0.280 · 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

Citations13
Published2021
Admission routes1
Has abstractyes

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