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Record W4313598009 · doi:10.1002/bin.1929

On the longevity of behavioral interventions for challenging behavior

2023· article· en· W4313598009 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBehavioral Interventions · 2023
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsGeorge Brown CollegeCentre for Addiction and Mental HealthBrock University
Fundersnot available
KeywordsPsychological interventionIntervention (counseling)PsychologyData qualityClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract The capacity for a treatment to maintain its effects over time may be the most critical component of behavioral interventions for challenging behavior as treatments that fail to persist are likely to be of little value to society. We reviewed the quality and quantity of different types of post‐intervention data for the treatment of challenging behavior in studies published over the last 7 years. We found that for the majority of participants at least one measure of maintenance, fading, or follow‐up was reported but with limited information regarding the quality of those measures. Reports of secondary variables related to post‐intervention data (e.g., latency to measurement) were also uncommon. We discuss possible explanations for the paucity of post‐intervention data, barriers to obtaining post‐intervention data, strategies for obtaining these data, and implications for the external validity of behavioral interventions for challenging behavior. We provide recommendations for increasing the probability that post‐intervention data are included in applied research on challenging 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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.885
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.696
GPT teacher head0.525
Teacher spread0.171 · 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