‘What a pity!’ – Exploring the use of ‘pitilho’ as harm reduction among crack users in Salvador, Brazil
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".