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Record W4410408711 · doi:10.1002/jaba.70013

Evaluating interactive computerized training to teach practitioners to implement firearm safety skills training

2025· article· en· W4410408711 on OpenAlexaff
Rasha R. Baruni, Raymond G. Miltenberger, Jennifer L. Cook, Anthony César Concepción, Trevor Maxfield

Bibliographic record

VenueJournal of Applied Behavior Analysis · 2025
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPsychologyCertificationFidelityMedical educationPsychological interventionAutism spectrum disorderTraining (meteorology)Applied psychologyInformation and Communications TechnologyProtocol (science)AutismMedicineComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

Researchers have shown that behavioral skills training (BST) and in situ training are effective for teaching firearm safety skills to children. Within the safety skills literature, there is evidence that manualized interventions are effective for teaching parents and teachers to conduct BST. An approach that has not been evaluated for teaching safety skills is interactive computerized training (ICT). The purpose of the current study was to evaluate an ICT program with three Board Certified Behavior Analysts (BCBA) who provided services to clients with autism spectrum disorder. In the final phase, the BCBAs implemented firearm safety skills training with their clients. Overall, the BCBAs implemented the safety skills training protocol with high fidelity during post-ICT assessments and rated the ICT program positively.

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)0.007
metaresearch head score (Gemma)0.024
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 score0.008
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.202
GPT teacher head0.474
Teacher spread0.272 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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