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Record W2943786515 · doi:10.54718/wdxu4301

Reducing Youth Risk Behaviors Through Interactive Theater Intervention

2016· article· en· W2943786515 on OpenAlexaff
Ryan J. Watson, Daniel McDonald, Ruth Carter, Joseph M. Russo

Bibliographic record

VenueJournal of Human Sciences and Extension · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIntervention (counseling)SocializationPsychologyMedical educationPositive Youth DevelopmentApplied psychologyDevelopmental psychologyMedicine

Abstract

fetched live from OpenAlex

The reduction of risk behaviors in secondary schools is a key concern for parents, teachers, and school administrators. School is one of the primary contexts of socialization for young people; thus, the investment in school-based programs to reduce risk behaviors is essential. In this study, we report on youth who participated in an intervention designed to improve decision-making skills based on positive youth development approaches. We examine changes in decision-making skills before and after involvement in the Teen Interactive Theater Education (TITE) program and retrospective self-assessment of change in knowledge, abilities, and beliefs as a result of participating in TITE (n = 127). Youth that reported increases in knowledge, abilities, and beliefs due to the intervention (n = 89) were more likely to think about the consequences of their decisions and list options before making a decision compared to their counterparts that reported less overall learning (n = 38). Implications for intervention research and stakeholders are discussed.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.060
GPT teacher head0.373
Teacher spread0.314 · 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 designNon-randomized trial
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

Citations1
Published2016
Admission routes1
Has abstractyes

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