Complex decision-making of people's use of opioid agonist therapy during pregnancy: Troubling the concept of ‘non-compliance’
Bibliographic record
Abstract
Clinical guidelines strongly recommend that pregnant people with opioid use disorder be offered Opioid Agonist Therapy (OAT); however, access to and retention in substance use services are influenced by a multitude of factors. We conducted 25 semi-structured qualitative interviews with pregnant and recently pregnant people who used opioids or OAT before their pregnancy. Participants were recruited from various health and social services, through social media and by word of mouth. For analysis, we used a voice-centred relational method; each transcript was read four times looking for different levels of influence in participants' stories (i.e., individual, interpersonal, societal). The participant's narratives highlighted how they navigated the expectations of 'good patient' and 'good mother.' We identified four trajectories of OAT use during pregnancy: two that represent 'compliance' and two that represent 'non-compliance.' While compliant patients used OAT out of concern for the health of their fetus and because it was medically recommended, the decision to not use OAT occurred in the context of weighing multiple, not only medical risks. Based on past experiences navigating health and social services, some participants made potentially medically risky decisions to avoid negative interactions with health and social care. Our results underscore that health and social service delivery is shaped by paternalistic measures aimed at controlling the behaviour of pregnant people who use drugs and reinforce the need for alternative approaches beyond control to foster more inclusive, supportive, and empathetic support for women who use drugs.
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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 | 0.028 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.037 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| 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".