Chronic pain among primary fentanyl users: The concept of self‐medication
Bibliographic record
Abstract
BACKGROUND: Chronic pain is among the leading causes of disability worldwide, of which only a small percentage of patients receive adequate treatment for. Non-prescribed opioid analgesics are commonly sought out in effort to alleviate unrelieved pain. This study assesses the prevalence and correlates of chronic pain among primary fentanyl users. METHODS: A cross-sectional and structured survey was conducted with 200 adults who reported fentanyl as their drug of choice from a Vancouver acute care hospital. Presence and levels of chronic pain were determined through self-report. RESULTS: The majority of participants (n = 130, 72.6%) reported having chronic pain in the past 6 months, with the mean level of pain on a typical day to be 7.6 out of a scale of 10 (SD = 1.9). Majority (n = 85, 65.4%) reported using street opioids to self-medicate, while only 9 (6.9%) reported that their chronic pain was unrelated. Regression analysis indicated that increasing age and co-use of cannabis and opioids were independent associated factors of chronic pain. Higher levels of reported pain on a typical day were further associated with age and self-medication. CONCLUSIONS: The findings of this study demonstrate a significant association between self-medication and chronic pain among primary fentanyl users in British Columbia. For these individuals, inadequate pain relief may drive continued opioid use, which in turn may increase risks of treatment discontinuation and overdose. Appropriate pain management strategies are crucial to avoid opioid misuse and decrease the large societal burden caused by chronic pain. SIGNIFICANCE: Our work points to the high prevalence of self-reported chronic pain among individuals who primarily use fentanyl. Among those with self-reported fentanyl use and chronic pain, self-medication with street opioids was found to be common and associated with higher reported pain levels on a typical day. This highlights the need for pain management strategies to be integrated into opioid dependence treatment and more research in the overlap of pain and fentanyl use.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".