Chronic Pain and Opioid Prescribing: Three Ways for Navigating Complexity at the Clinical‒Population Health Interface
Bibliographic record
Abstract
Clinically focused interventions for people living with pain, such as health professional education, clinical decision support systems, prescription drug monitoring programs, and multidisciplinary care to support opioid tapering, have all been promoted as important solutions to the North American opioid crisis. Yet none have so far delivered substantive beneficial opioid-related population health outcomes. In fact, while total opioid prescribing has leveled off or reduced in many jurisdictions, population-level harms from opioids have continued to increase dramatically. We attribute this failure partly to a poor recognition of the epistemic and ethical complexities at the interface of clinical and population health. We draw on a framework of knowledge networks in wicked problems to identify 3 strategies to help navigate these complexities: (1) designing and evaluating clinically focused interventions as complex interventions, (2) reformulating evidence to make population health dynamics apparent, and (3) appealing to the inseparability of facts and values to support decision-making in uncertainty. We advocate that applying these strategies will better equip clinically focused interventions as complements to structural and public health interventions to achieve the desired beneficial population health effects. (Am J Public Health. 2022;112(S1):S56–S65. https://doi.org/10.2105/AJPH.2021.306500 )
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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 | 0.086 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.016 | 0.141 |
| Scholarly communication | 0.047 | 0.077 |
| Open science | 0.004 | 0.047 |
| Research integrity | 0.014 | 0.024 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".