Characterization of Bodily Pain and Use of Both Prescription and Non-Prescription Opioids in Tenants of Precarious Housing
Bibliographic record
Abstract
Background opioid use, which includes both prescribed and non-prescribed drugs, is relatively common amongst marginalized populations. Past research has shown that among those who use non-prescribed or diverted opioids recreationally, many were first exposed to the drug as prescribed pain medication. Objective: to better understand the relationship between pain and opioid use in tenants of precarious housing. Methods: in the present study, 440 individuals from a cohort living in homeless or precariously housed conditions in a neighborhood with high rates of poverty and drug use were interviewed for their bodily pain and opioid use. We examined the relationship between bodily pain levels, assessed using the Maudsley Addiction Profile questionnaire, and prescribed, non-prescribed and combined self-reported opioid use in the prior 28 days assessed using the Timeline Followback and Doctor-Prescribed Medication Timeline Followback questionnaires. Results: Analysis of the results indicated that sex (female), age (younger) and early exposure to opioids (≤ age 18) predicted current opioid use, but there was no association between current bodily pain levels and opioid use. Conclusions: these unexpected findings indicate the complex nature of the relationship between pain and opioid use in this population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".