The Social Organization of Opioid Use for Chronic Pain
Bibliographic record
Abstract
This dissertation is an institutional ethnography of the social organization of opioid use for chronic pain management in Ontario, Canada. Within the context of the contemporary ‘opioid crisis,’ people with chronic pain have found their access to prescription opioids restricted as physicians rapidly taper patients’ doses, adopt ‘no narcotics policies,’ and bar people with chronic pain who use opioids from their practices. These changes in physicians’ work of prescribing opioids have had significant impacts on the lives of people with chronic pain. \n \nTo explicate these abrupt changes in patients’ access to opioids, I conducted interviews with people with chronic pain and with health care practitioners, as well as analyses of texts including prescribing guidelines. I found that physicians have changed their opioid prescribing practices since the late aughts as the medical profession has come to be targeted as responsible for increases in opioid-related harms in North America. While previous panics around drug use targeted people who use drugs and attempted to change their behaviour, recent shifts in medico-legal conceptions of opioids have meant that opioid users have emerged in the public imaginary as blameless victims of unscrupulous physicians. Strategies to resolve the ‘epidemic’ of opioid-related harms have focused on changing physicians’ prescribing practices through new forms of opioid pharmacovigilance, surveillance, and punishment. \n \nIn response to these modes of surveillance and punishment, physicians have responded by adopting work practices that demonstrate their compliance with and accountability to these regimes of ruling. These include tapering patients’ doses and refusing to prescribe opioids to any patients ‘tainted’ by social determinants of health such as poverty or racialization. While people with chronic pain are not directly targeted by interventions to end the opioid crisis, they are impacted by these policies as physicians change their opioid prescribing practices in response to heightened surveillance and risk of penalties such as losing their license to practice or public shaming.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.023 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".