Factors associated with potentially inappropriate opioid use in community‐living older adults consulting in primary care
Bibliographic record
Abstract
OBJECTIVE: To study the factors associated with opioid use and potentially inappropriate opioid use (PIOU) in primary care older adults with non-cancer pain referring to the conceptual framework developed by the American Agency for Healthcare Research and Quality. METHODS: This is a secondary analysis of health survey and medico-administrative data from Québec, Canada. Individuals aged ≥65 were recruited between 2011 and 2013 in primary care clinics to participate in face-to-face interviews. The sample included 945 older adults without a malignant tumor over the study period or any tumor in the 2 years surrounding opioid use. Opioid use within a 3 year follow-up period was identified from the public drug plan database. Potentially inappropriate opioid use (PIOU) was defined using the American Geriatrics Society Beers 2019 list. Multinomial regression analyses were performed to study the factors (patient, pain, substance use, provider, healthcare system) associated with opioid use and PIOU. RESULTS: In this sample of older adults, 26.2% used an opioid and 18.4% were categorized as PIOU. Factors associated with PIOU compared to opioid use included female sex, higher psychological distress, number of emergency department visits, and recruitment type of healthcare practice. Factors associated with PIOU compared to no use included female sex, country of origin, presence of a trauma, physical/psychiatric multimorbidity, number of outpatient consultations, pain severity/type, and number of prescribers. CONCLUSIONS: Mental health and health system factors were associated with PIOU. Results highlights the importance of a multidisciplinary approach for pain management, and the urgent need for implementing organizational efforts to optimize opioid use in primary care.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".