Contraceptive decision making among pregnancy-capable individuals with opioid use disorder at a tertiary care center in Massachusetts
Bibliographic record
Abstract
OBJECTIVE: To explore contraceptive decision making among recently pregnant patients with a history of opioid use disorder. STUDY DESIGN: We conducted semi-structured qualitative interviews, based on principles of the Ottawa Decision Support Framework, with 20 recently pregnant individuals diagnosed with opioid use disorder at a tertiary care medical center in Massachusetts. We audio-recorded the interviews and they were transcribed verbatim. We analyzed our interview data using inductive and deductive coding. RESULTS: Participants value the availability of barrier methods as a means of preventing both sexually transmitted infections and pregnancy. For some participants, housing instability makes storing contraceptive methods and managing personal hygiene related to bleeding patterns difficult. For others, housing instability impacts their overall fertility goals. Side effects including weight gain, interactions with mood stabilizing medications, concern regarding post-operative opioids, or intrinsic aspects of a method that serve as reminders of opioid use may be unacceptable given the risk of relapse. The relapsing and remitting arc of recovery make remembering important aspects of both short- and long-acting contraceptive method use difficult, yet participants offer strategies to aid in doing so. CONCLUSION: When choosing a contraceptive method participants in our study exhibit similarities to individuals with other chronic medical conditions as well as motivations specific to opioid use disorder. Their contraceptive decisions are grounded in integrating a method into a chaotic life, preventing relapse, and protecting future fertility. IMPLICATIONS: Our data highlight how lived experiences at the intersection of active opioid use disorder and recovery fundamentally shape the lens through which pregnancy-capable individuals with opioid use disorder view their contraceptive decisions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".