Controlling Chaos: The Perceptions of Long-Term Crack Cocaine Users in Vancouver, British Columbia, Canada
Bibliographic record
Abstract
People who smoke crack cocaine are described as chaotic and more likely to engage in risky sex, polysubstance use and contract infectious diseases. However, little is known about how individuals perceive smoking crack as compared to other forms of cocaine use, especially injection. We explored the lived experience of people who smoke crack cocaine. Six gender-specific focus groups (n = 31) of individuals who currently smoke crack in Vancouver, Canada, were conducted using a semi-structured interview guide. Focus groups were transcribed and analyzed by constant comparative methodology. We applied Rhodes' risk environment to the phenomenological understanding that individuals have regarding how crack has affected their lives. Subjects reported that smoking rather than injecting cocaine allows them to begin "controlling chaos" in their lives. Controlling chaos was self-defined using nontraditional measures such as the ability to maintain day-to-day commitments and housing stability. The phenomenological lens of smoking crack instead of injecting cocaine "to control chaos" contributes a novel perspective to our understanding of the crack-smoking population. This study examines narratives which add to prior reports of the association of crack smoking and increased chaos and suggests that, for some, inhaled crack may represent efforts towards self-directed harm reduction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".