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Record W2102512049 · doi:10.3109/09687637.2010.506898

‘What a pity!’ – Exploring the use of ‘pitilho’ as harm reduction among crack users in Salvador, Brazil

2010· article· en· W2102512049 on OpenAlexaff
Tarcísio Andrade, Laita Santiago, Erica Amari, Benedikt Fischer

Bibliographic record

VenueDrugs Education Prevention and Policy · 2010
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsCentre for Addiction and Mental HealthSimon Fraser University
Fundersnot available
KeywordsOutreachHarm reductionHarmVulnerability (computing)Work (physics)PsychologyEnvironmental healthMedicineSocial psychologyComputer securityNursingEngineeringComputer sciencePolitical sciencePublic health

Abstract

fetched live from OpenAlex

Aims: The aim of this study was to explore the use of ‘pitilho’ (the co-smoking of crack and marijuana in a cigarette-like form) among crack users in Salvador, Brazil as a potential harm reduction measure.Methods: In-depth interviews were conducted with two outreach workers who frequently encountered the use of ‘pitilho’ as part of their community outreach programme work, as well as four ‘pitilho’ users who were clients of the programme. Daily field notes were also collected. Transcribed data were analysed for common reasons for ‘pitilho’ use.Findings: Several key reasons crack users have adopted the ‘pitilho’ as a harm reduction tool were uncovered: it was reported to reduce the negative pharmaco-behavioural and physical effects of crack use, is more economical, provides users with better control over their behaviours, and decreases their vulnerability for violence and betters their sub-cultural position.Conclusions: ‘Pitilho’ may offer several relevant short-term benefits to users and therefore may constitute a potentially important ‘harm reduction’ tool in an area where little other targeted prevention measures exist. Our exploratory data need to be investigated in depth by appropriate and rigorous methods.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.070
GPT teacher head0.395
Teacher spread0.325 · 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 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

Citations23
Published2010
Admission routes1
Has abstractyes

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