‘Planning for a healthy baby and a healthy pregnancy’: A critical analysis of Canadian clinical practice guidelines for the treatment of opioid dependence during pregnancy
Bibliographic record
Abstract
As opioid fatalities rise in North America, the need to improve the supports available to those who are dependent on opioids and pregnant has become more urgent. This paper discusses the social organisation of drug treatment supports for those who are pregnant, using Canadian clinical practice guidelines (CPGs) for methadone maintenance treatment (MMT) as a case study. Pregnant patients are a priority population for MMT, both in Canada and internationally; the regulatory bodies that oversee MMT in Canada are the provincial Colleges of Physician and Surgeons and Health Canada. The paper analyses MMT CPGs published by these agencies, comparing their general recommendations to those specific to pregnant patients. We demonstrate that the guidelines address few treatment considerations for pregnant patients, other than improved birth outcomes and child welfare, despite acknowledging their more complex needs. Drawing on social science studies of gender and drugs, we argue that MMT CPGs therefore perpetuate the intensified surveillance and foetal prioritisation that have long generated barriers to care for opiate-dependent pregnant patients. We also discuss how and why the CPGs ultimately only reinforced these current limitations in the drug treatment sector.
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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 | 0.085 | 0.270 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.001 | 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".