MétaCan
Menu
Back to cohort
Record W2116157222 · doi:10.2190/hfdb-3xdg-59d1-gg8p

Results of a Pilot Program for Training Bar Staff in Preventing Aggression

2000· article· en· W2116157222 on OpenAlexaffabout
M Coutts, Kathryn Graham, Kathy Braun, Samantha Wells

Bibliographic record

VenueJournal of Drug Education · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicStalking, Cyberstalking, and Harassment
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsAggressionPsychologyBehavior changeMedical educationApplied psychologyTraining (meteorology)Clinical psychologyMedicineSocial psychology

Abstract

fetched live from OpenAlex

One hundred and twenty-one staff from eight bars in Ontario, Canada participated in a three-hour training program that used a peer learning model to teach problem-solving skills regarding the prevention and management of aggressive behavior in bars. Participants showed significant positive changes in knowledge and attitudes regarding effective approaches to preventing aggression. The majority of participants reported that the training made them think about ways they handed problem situations and that they would change the way they handle problems in the future. Participants rated most aspects of the training as very useful, especially the group discussion. The program illustrates the potential for increasing skills and knowledge of bar staff in preventing aggression and associated injury.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.134
Threshold uncertainty score0.266

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.039
GPT teacher head0.383
Teacher spread0.344 · 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

Citations20
Published2000
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Drug EducationSame topicStalking, Cyberstalking, and HarassmentFrench-language works237,207