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Uso de drogas ilícitas e perspectivas críticas de familiares e pessoas próximas na cidade do Rio de Janeiro - Zona Norte, Brasil

2009· article· pt· W2102632702 on OpenAlexaff
Cristina Maria Douat Loyola, Bruna Brands, Edward M. Adlaf, Norman Giesbrecht, Laura Simich, Maria da Glória Miotto Wright

Bibliographic record

VenueRevista Latino-Americana de Enfermagem · 2009
Typearticle
Languagept
FieldSocial Sciences
TopicYouth, Drugs, and Violence
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

This article presents the partial results of a multicenter, cross-temporal study, which was performed using multiple methods, and involved seven Latin-American countries and Canada. The results presented refer to the city center of Rio de Janeiro (n=108). The central question of the study was: 'How do illicit drug users' relatives and acquaintances describe protective and risk factors, prevention initiatives, treatment services, laws and policies regarding illicit drugs?' The quantitative data was collected using an instrument containing closed questions. In total, 108 young adults (18 years of age or older) were interviewed, who stated being affected by the drug although they were not users. For 104 interviewees (96%), negligence is the family dynamics that causes the greatest exposure to drugs, and 106 (98%) consider that parent support is what offers the greatest protection. Policies, the police and the criminal system have neither reduced drug use nor do they protect users.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.216
Threshold uncertainty score0.429

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.349
Teacher spread0.313 · 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 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

Citations7
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueRevista Latino-Americana de Enfermagem→Same topicYouth, Drugs, and Violence→French-language works237,207→