A conceptual framework for identifying and managing system vulnerabilities for diversion of controlled substances in healthcare
Bibliographic record
Abstract
PURPOSE: Diversion or theft of controlled substances is a recognized problem affecting healthcare systems globally. The purpose of this study was to develop a framework for identifying and characterizing system factors leading to vulnerabilities for diversion within hospitals. METHODS: We applied a qualitative framework method, which involved 1) compiling a list of critical diversion vulnerabilities through observations and proactive risk analyses in the inpatient pharmacy, emergency department and intensive care unit of two Canadian hospitals; 2) coding the vulnerabilities into deductively and inductively derived themes and subthemes; and 3) building a conceptual framework. RESULTS: Our framework for diversion demonstrates how mitigating downstream diversion outcomes (e.g., harms to patients, healthcare workers, and institutions) requires the redesign of upstream system factors associated with pilfering and forgery processes. We identified 20 subthemes associated with the following five overarching themes of system factors contributing to diversion risk: task (e.g., variation in how work was done or lack of verification), person (e.g., use of insider knowledge or collaboration among staff), tools/technologies (e.g., limitations of electronic systems to identify discrepancies), organization (e.g., cultural/behavioural norms or hospital policies for controlled substance management), and internal environment (e.g., layout of the space). CONCLUSION: The Diversion Framework is a conceptual model developed for use by practitioners, researchers, and policy makers to identify system factors and analyze medication-use processes that may be vulnerable to diversion. This in turn can inform safeguards to prevent harm to patients, healthcare workers and the institution.
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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 | 0.032 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.006 | 0.005 |
| 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".