Bridging the evidence and the politics: Implementation trial of supervised injectable opioid treatment (SIOT) in Australia
Bibliographic record
Abstract
Supervised Injectable Opioid Treatment (SIOT) targets people experiencing opioid dependence who have not benefited from existing treatments. In this population, SIOT has been demonstrated to be efficacious and effective, yet this modality of treatment has only been taken up in a few countries. In this commentary we describe the socio-political context and history to the recent establishment of an implementation trial of injectable hydromorphone in Sydney, Australia. These factors influenced choices about the trial design, including integration of SIOT within an existing opioid agonist treatment program, time-limited treatment, and an assessment of feasibility, acceptability, safety and cost. While all new drug policy initiatives occur within a specific socio-political and historical context, we hope this commentary provides reflections for other places considering the introduction of SIOT.
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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 | 0.130 | 0.237 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.029 | 0.037 |
| Insufficient payload (model declined to judge) | 0.003 | 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".