We need to talk: The urgent conversation on chronic pain, mental health, prescribing patterns and the opioid crisis
Bibliographic record
Abstract
The opioid crisis' pathways from first exposure onwards to eventual illnesses and fatalities are multiple, intertwined and difficult to dissect. Here, we offer a multidisciplinary appraisal of the relationships among mental health, chronic pain, prescribing patterns worldwide and the opioid crisis. Because the opioid crisis' toll is especially harsh on young people, emphasis is given on data regarding the younger strata of the population. Because analgesic opioid prescription constitute a recognised entry point towards misuse, opioid use disorder, and ultimately overdose, prescribing patterns across different countries are examined as a modifiable hazard factor along these pathways of risk. Psychiatrists are called to play a more compelling role in this urgent conversation, as they are uniquely placed to provide synthesis and lead action among the different fields of knowledge and care that lie at the crossroads of the opioid crisis. Psychiatrists are also ideally positioned to gauge and disseminate the foundations for diagnosis and clinical management of mental conditions associated with chronic pain, including the identification of hazardous and protective factors. It is our hope to spark more interdisciplinary exchanges and encourage psychiatrists worldwide to become leaders in an urgent conversation with interlocutors from the clinical and basic sciences, policy makers and stakeholders including clients and their families.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".