Exploring challenges and opportunities in detecting emerging drug trends: A socio-technical analysis of the Canadian context
Bibliographic record
Abstract
OBJECTIVES: This study aimed to apply a systems thinking approach to explore factors influencing the detection of emerging drug trends in Canada's provinces and territories to better understand how the local context can influence the design and performance of a pan-Canadian (i.e., national) substance use early warning system (EWS). This study also presents a set of actionable recommendations arising from the results. METHODOLOGY AND METHODS: Semi-structured interviews were conducted with 13 purposively recruited Medical Officers of Health and epidemiologists from across Canada working in the field of substance use. Thematic and social network analysis guided by the socio-technical systems framework were subsequently employed. RESULTS: Barriers and facilitators for detecting emerging drug trends in provinces and territories are a product of the collective linkages and interactions between social (objectives, people, culture), technical (tools, practices, infrastructure), and external environmental (financial, regulatory frameworks, stakeholders) factors. Shortcomings in several of these areas shaped the system's behaviour and together contributed to fragmented operations that lacked strategic focus, poorly designed cross-sector partnerships, and unactionable information outputs. Participants' experiences shaped perceptions of a national substance use EWS, with some voicing potential opportunities and others expressing doubts about its effectiveness. CONCLUSION: This study highlights interconnected social, technical, and external environmental considerations for the design and implementation of a national substance use EWS in Canada. It also demonstrates the value of using the socio-technical systems framework to understand a complex public health surveillance issue and how it can be used to inform a path forward.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.026 | 0.011 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".