Exploring the Diversion of Controlled Substances in Canadian Hospital Settings Using Social Network Analysis
Bibliographic record
Abstract
Introduction: The diversion or theft of controlled substances (CS) is a well-known issue that affects healthcare systems worldwide. These substances are often stolen from healthcare facilities through various integrated and diverse mechanisms. While research to date has largely focused on physical security vulnerabilities; there is limited understanding of the role healthcare workers’ (HCWs) social networks play in facilitating this diversion. Aims: To use social network analysis (SNA) to gain a comprehensive understanding of the social structures within hospital medication use processes (MUPs) susceptible to controlled substance diversion by HCWs and to identify network areas for improvement. Study Design/Approach: Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews, I conducted a scoping review of empirical literature to explore the use of SNA methods in process improvement within healthcare (Study 1). I applied two-mode SNA in two Ontario hospitals in the inpatient pharmacy (Study 2) and emergency department (ED) (Study 3) to map the social network structures, identify connections susceptible to diversion, and highlight opportunities for improvement. Finally, I performed a single case study (Study 4) with a physician who diverted to describe their processes and the social connections that were exploited. Results: Study 1 demonstrated that SNA methods were crucial for identifying essential work processes and revealing bottlenecks in organizational workflow. This provides insight into resource allocation, team performance, influential providers, and process improvement effectiveness. Studies 2 and 3 applied these findings and highlighted the interdependencies between HCWs (within and across roles) and how these social connections, or lack thereof, can contribute to or mitigate diversion. Study 4 identified six diversion themes and key actors related to exploitative mechanisms in healthcare networks. Conclusion: My thesis builds on previous efforts to raise awareness of vulnerabilities to drug diversion in Canadian hospitals. It focuses on understanding the social connections among HCWs that are often ignored but contribute to diversion. SNA was used to demonstrate how understanding social networks can strengthen hospitals’ ability to identify effective interventions for preventing diversion and securing controlled substances.
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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 | 0.020 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".