Perceived risk of opioid use disorder secondary to opioid analgesic medication use by the general population in France
Bibliographic record
Abstract
BACKGROUND: In Europe and France, the use of opioid analgesic drugs has become widespread as an option for pain management. However, their use can lead to nonmedical use and/or opioid use disorder (OUD). This work aimed to assess the perceived risk of OUD secondary to opioid analgesic drugs use by the general population. METHODS: We conducted a cross-sectional observational study using the GrippeNet web-based cohort, comprising about 10,000 French volunteers from the general population, using a self-administered questionnaire. The main outcome was the perceived risk of OUD secondary to opioid analgesic drugs use, assessed by a 4-item scale and modelled using logistic regression (backward procedure). RESULTS: Among 5046 French respondents, after adjustment, 65% believed that the use of analgesic drugs could likely or very likely lead to OUD. Factors associated with the perception of a higher risk were being over 50 and having heard about opioids in the media. Previous opioid use and a high level of education decreased the perception of the risk. Among those having used opioids in the past 2 years (N = 1770), 71.1% reported being not at all concerned by this risk. The majority of the sample perceived the risk of OUD but those having already used opioid analgesics drugs expressed no concern about this risk for themselves. CONCLUSIONS: This finding highlight the need to reinforce warning on the package insert documents, therapeutic education and collaborative care between the prescribing general practitioners and pharmacists to increase awareness of opioid medications users on the risk of OUD. SIGNIFICANCE: This study found that the risk of OUD secondary to opioid analgesics use is well perceived in the general population, but that those having already used opioid analgesics expressed little concern for themselves. This finding could potentially help to raise awareness of healthcare providers and policy makers on the lack of perceived risk regarding these drugs and the need to inform and identify at-risk patients in primary care.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".