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Record W4399378171 · doi:10.2196/58322

Using the Preparation Phase of the Multiphase Optimization Strategy to Design an Antiextremism Program in Bahrain: Formative and Pilot Research

2024· article· en· W4399378171 on OpenAlexvenueno aff
Kelly L. Rulison, GracieLee Weaver, Jeffrey J. Milroy, Emily Beamon, Samantha E. Kelly, Ali Ameeni, Amina Juma, Fadhel Abualgasim, Jaafar Husain, David L. Wyrick

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsnot available
Fundersnot available
KeywordsFormative assessmentPhase (matter)Computer scienceEngineeringManagement scienceEngineering managementPsychologyMathematics educationChemistry

Abstract

fetched live from OpenAlex

BACKGROUND: Extremism continues to raise concerns about conflict and violent attacks that can lead to deaths, injuries, trauma, and stress. Adolescents are especially vulnerable to radicalization by extremists. Given its location in a region that often experiences extremism, Bahrain developed 4 peaceful coexistence lessons and 4 antiextremism lessons to be implemented as part of their Drug Abuse Resistance Education (D.A.R.E.) program. OBJECTIVE: The aim of this study is to report the results of the preparation phase of the multiphase optimization strategy (MOST) to develop a peaceful coexistence program and an antiextremism program implemented by D.A.R.E. officers in Bahrain. METHODS: We developed conceptual models for the peaceful coexistence and antiextremism programs, indicating which mediators each lesson should target, the proximal outcomes that should be shaped by these mediators, and the distal and ultimate outcomes that the intervention should change. We recruited 20 middle schools to pilot test our research protocol, survey measures, and the existing intervention lessons. A total of 854 seventh and ninth grade students completed a pretest survey, 4 peaceful coexistence intervention lessons, and an immediate posttest survey; and a total of 495 ninth grade students completed the pretest survey, 4 antiextremism lessons, and an immediate posttest survey. A series of 3-level models, nesting students within classrooms within schools, tested mean differences from pretest to posttest. RESULTS: Pilot test results indicated that most measures had adequate reliability and provided promising evidence that the existing lessons could change some of the targeted mediators and proximal outcomes. Specifically, students who completed the peaceful coexistence lessons reported significant changes in 5 targeted mediating variables (eg, injunctive norms about intolerance, P<.001) and 3 proximal outcomes [eg, social skills empathy (P=.008); tolerance beliefs (P=.041)]. Students who completed the antiextremism lessons reported significant changes in 3 targeted mediators [eg, self-efficacy to use resistance skills themselves (P<.001)], and 1 proximal outcome (ie, social skills empathy, P<.001). CONCLUSIONS: An effective antiextremism program has the potential to protect youth from radicalization and increase peaceful coexistence. We used the preparation phase of MOST to (1) develop a conceptual model, (2) identify the 4 lessons in each program as the components we will evaluate in the optimization phase of MOST, (3) pilot test the existing lessons, our newly developed measures, and research protocol, and (4) determine that our optimization objective will be all effective components. We will use these results to revise the existing lessons and conduct optimization trials to evaluate the efficacy of the individual lessons.

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)0.019
metaresearch head score (Gemma)0.025
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 score0.019
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.025
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.394
GPT teacher head0.591
Teacher spread0.198 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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