Perceptions of Signs of Addiction Among Opioid Naive Patients Prescribed Opioids in the Emergency Department
Bibliographic record
Abstract
OBJECTIVES: Patient knowledge deficits related to opioid risks, including lack of knowledge regarding addiction, are well documented. Our objective was to characterize patients' perceptions of signs of addiction. METHODS: This study utilized data obtained as part of a larger interventional trial. Consecutively discharged English-speaking patients, age >17 years, at an urban academic emergency department, with a new opioid prescription were enrolled from July 2015 to August 2017. During a follow-up phone interview 7 to 14 days after discharge, participants were asked a single question, "What are the signs of addiction to pain medicine?" Verbatim transcribed answers were analyzed using a directed content analysis approach and double coding. These codes were then grouped into themes. RESULTS: There were 325 respondents, 57% female, mean age 43.8 years, 70.1% privately insured. Ten de novo codes were added to the 11 DSM-V criteria codes. Six themes were identified: (1) effort spent acquiring opioids, (2) emotional and physical changes related to opioid use, (3) opioid use that is "not needed, (4) increasing opioid use, (5) an emotional relationship with opioids, and (6) the inability to stop opioid use. CONCLUSIONS: Signs of addiction identified by opioid naive patients were similar to concepts identified in medical definitions. However, participants' understanding also included misconceptions, omissions, and conflated misuse behaviors with signs of addiction. Identifying these differences will help inform patient-provider risk communication, providing an opportunity for counseling and prevention.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".