Establishing the Melbourne injecting drug user cohort study (MIX): rationale, methods, and baseline and twelve-month follow-up results
Bibliographic record
Abstract
BACKGROUND: Cohort studies provide an excellent opportunity to monitor changes in behaviour and disease transmission over time. In Australia, cohort studies of people who inject drugs (PWID) have generally focused on older, in-treatment injectors, with only limited outcome measure data collected. In this study we specifically sought to recruit a sample of younger, largely out-of-treatment PWID, in order to study the trajectories of their drug use over time. METHODS: Respondent driven sampling, traditional snowball sampling and street outreach methods were used to recruit heroin and amphetamine injectors from one outer-urban and two inner-urban regions of Melbourne, Australia. Information was collected on participants' demographic and social characteristics, drug use characteristics, drug market access patterns, health and social functioning, and health service utilisation. Participants are followed-up on an annual basis. RESULTS: 688 PWID were recruited into the study. At baseline, the median age of participants was 27.6 years (IQR: 24.4 years - 29.6 years) and two-thirds (67%) were male. Participants reported injecting for a median of 10.2 years (range: 1.5 months - 21.2 years), with 11% having injected for three years or less. Limited education, unemployment and previous incarceration were common. The majority of participants (82%) reported recent heroin injection, and one third reported being enrolled in Opioid Substitution Therapy (OST) at recruitment. At 12 months follow-up 458 participants (71% of eligible participants) were retained in the study. There were few differences in demographic and drug-use characteristics of those lost to follow-up compared with those retained in the study, with attrition significantly associated with recruitment at an inner-urban location, male gender, and providing incomplete contact information at baseline. CONCLUSIONS: Our efforts to recruit a sample of largely out-of-treatment PWID were limited by drug market characteristics at the time, where fluctuating heroin availability has led to large numbers of PWID accessing low-threshold OST. Nevertheless, this study of Australian injectors will provide valuable data on the natural history of drug use, along with risk and protective factors for adverse health outcomes associated with injecting drug use. Comprehensive follow-up procedures have led to good participant retention and limited attrition bias.
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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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".