Opioid agonist therapy during pregnancy: A feminist community-engaged mixed methods study
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. I conducted a community-engaged, convergent mixed methods study to 1) Characterize the patterns and trajectories of OAT (methadone and buprenorphine) use behaviour during pregnancy in Ontario; 2) Explore the complex and context-specific factors that influence the use of OAT during pregnancy; and 3) Describe how socially organized power relations impact people’s autonomy and decision-making about OAT. I embraced feminist standpoint theory as an onto-epistemology and methodology. Feminist standpoint theorists have challenged the power imbalances inherent in knowledge production and proposed that all knowledge is socially situated. To describe the diverse material and social conditions of pregnant people who use opioids I used two sources of data: a quantitative analysis of population health data and qualitative interviews. For the quantitative stream, I analyzed linked ICES databases to quantify trends in OAT prescribing over time in Ontario, and the impact of three population-level interventions on these trends. For the qualitative stream, I conducted interviews with pregnant or recently pregnant people who used opioids or OAT before their pregnancy. Then, through interpretive focus groups with a Community Advisory Board of people with lived/living experience of pregnancy and OAT use, I used the results of these two streams of research to investigate how health and social systems function in the lives of pregnant people who use drugs as well as the ideology and conceptual practices used to uphold and justify these systems. By examining the complex decision-making processes and agency of pregnant people using OAT and exploring the paradoxes and complexities they face, this study challenges conventional understandings of 'non-compliance' and 'resistance.' My results underscore that health and social service delivery is shaped by paternalistic measures aimed at controlling the behaviour of pregnant people who use drugs. The findings 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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".