Estimating the number of injection drug users in greater Victoria, Canada using capture-recapture methods
Bibliographic record
Abstract
BACKGROUND: Population size estimation is critical for planning public health programmes for injection drug users. Estimation is difficult, as these populations are considered 'hidden' or 'hard to reach'. The currently accepted population size estimate for greater Victoria, Canada is between 1,500 and 2,000 individuals, which is dated prior to the year 2000, and is likely an underestimate. METHODS: We used three mark-recapture methods (the Lincoln-Petersen estimator, Huggins' model, and Pledger's model) to estimate population size using cross-sectional survey data collected in 2003 and 2005. Data come from a closed population with two time-ordered samples from the same source. We compare our estimates with the currently accepted estimate that is based on the registry of a Victoria needle exchange. RESULTS: All methods provided population size estimates that were higher than the currently accepted estimate. Huggins' method produced wider confidence intervals. Point estimates of population size from the three methods ranged from 3,329 to 3,342. CONCLUSIONS: Our estimates will aid health authorities in planning for harm reduction programmes. Repeating the methods as further phases of I-Track data become available will ensure that the population estimates remain up to date.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".