Challenges for the implementation of injectable opioid agonist treatment: a scoping review
Bibliographic record
Abstract
BACKGROUND AND AIMS: Injectable opioid agonist treatment (iOAT) is a valuable, patient-centred, evidence based intervention. However, limited information exists on contextual factors that may support or hinder iOAT implementation and sustainability. This study aims to examine existing research on iOAT using diacetylmorphine and hydromorphone, focusing on identifying the key barriers and facilitators to its successful implementation. METHODS: A systematic search was conducted in the MEDLINE and PsycInfo databases (via Ovid) from inception to February 2024, supplemented by a comprehensive grey literature search. No restrictions were applied regarding publication type, year, or geographic location. Articles were independently screened by two reviewers. Eligible articles described the feasibility, implementation, and/or evaluation of iOAT in one or more countries, presenting perspectives on receiving, administering, or governing iOAT. RESULTS: Forty-four publications were selected for inclusion. Barriers identified through thematic analysis included public acceptance concerns such as medication diversion, increased crime, and the Honey-Pot effect. Legal and ethical challenges identified involved enacting changes in law to make certain substances available as a medically controlled options for treatment, and addressing patient consent issues. Negative media coverage and public controversies were found to undermine acceptance, and high start-up costs especially for security, facility access, and economic feasibility were seen as additional obstacles. Regulatory barriers and stringent protocols were the most frequently cited limiting factors by patients and providers. Facilitators included the integration of trial prescriptions into comprehensive drug policy strategies and publishing data for evidence-based debates, together with ethics committees ensuring compliance with ethical standards. Developing information strategies and addressing opponents' claims improved public perception. Cost-effectiveness evidence was found to support long-term implementation, while flexible treatment protocols, inclusive spaces, and affirming therapeutic relationships were seen as important facilitators to enhance patient engagement and treatment effectiveness. CONCLUSIONS: Successful implementation of iOAT requires balancing political and social acceptability with scientific integrity, alongside strategic communication and public outreach. Further research is needed to enhance the transferability of findings across diverse socio-political contexts and address key influencing factors associated with iOAT programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".