A concept mapping study of service user design of safer supply as an alternative to the illicit drug market
Bibliographic record
Abstract
Within North America and worldwide, drug-related overdoses have increased dramatically over the past decade. COVID-19 escalated the need for a safer supply of illicit substances to reduce overdoses with hopes of replacing substances obtained from the illicit drug market. Drug users 1 1 The terms drug users and people who use drugs are recommended by our drug user organization co-authors and the national body of drug user organizations.Declarations of Interest should be at the centre of program and policy decisions related to the development and implementation of safer supply. Yet, there is little empirical research that conceptualizes effective safer supply from their perspectives. Within a community based participatory approach to research, we conducted a concept mapping study to foreground the perspectives of drug users and develop a conceptual model of effective safer supply. Our team was composed of researchers from a local drug user organization, a local harm reduction organization, and academic researchers. The focused prompt developed by the team was: “Safe supply would work well if…” Sixty-three drug users participated in three rounds of focus groups as part of the concept mapping process, involving brainstorming, sorting, rating and naming of themes. The concept mapping process resulted in six clusters of statements: 1) Right dose and right drugs for me; 2) Safe, positive and welcoming spaces; 3) Safer supply and other services are accessible to me; 4) I am treated with respect; 5) I can easily get my safer supply; and 6) Helps me function and improves my quality of life (as defined by me). The statements within each cluster describe key components central to an effective model of safer supply as defined by drug users. The results of this study provide insights into key components of effective safer supply to inform planning and evaluation of future safer supply programs informed by drug user perspectives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".