MétaCan
Menu
Back to cohort
Record W2576999608 · doi:10.1186/s12954-017-0132-7

Perceptions of a drug prevention public service announcement campaign among street-involved youth in Vancouver, Canada: a qualitative study

2017· article· en· W2576999608 on OpenAlexafffundabout
Lianlian Ti, Danya Fast, William Small, Thomas Kerr

Bibliographic record

VenueHarm Reduction Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of British Columbia HospitalSt. Paul's HospitalSimon Fraser University
FundersNational Institute on Drug AbuseCanadian Institutes of Health ResearchNational Institutes of HealthMichael Smith Health Research BC
KeywordsHealth psychologyPublic healthQualitative researchPerceptionSocial policySocial workService-learningDrug preventionService (business)Public relationsMedicinePsychologyCriminologySociologyPolitical scienceSubstance abuseNursingPsychiatryPedagogyBusiness

Abstract

fetched live from OpenAlex

BACKGROUND: Due to the popularity of public service announcements (PSAs), as well as the broader health and social harms associated with illicit drug use, this study sought to investigate how drug prevention messages found in the Government of Canada's DrugsNot4Me campaign were understood, experienced, and engaged with among a group of street-involved young people in Vancouver, Canada. METHODS: Qualitative interviews were conducted with 25 individuals enrolled in the At-Risk Youth Study, and a thematic analysis was conducted. RESULTS: Findings indicate that the campaign's messages neither resonated with "at-risk youth", nor provided information or resources for support. In some cases, the messaging exacerbated the social suffering experienced by these individuals. CONCLUSIONS: This study underscores the importance of rigorous evaluation of PSAs and the need to consider diverting funds allocated to drug prevention campaigns to social services that can meaningfully address the structural drivers of drug-related harms among vulnerable youth populations.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.786
Threshold uncertainty score0.947

Codex and Gemma teacher scores by category

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

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 head0.068
GPT teacher head0.345
Teacher spread0.277 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations12
Published2017
Admission routes3
Has abstractyes

Explore more

Same venueHarm Reduction JournalSame topicSubstance Abuse Treatment and OutcomesFrench-language works237,207