Trusted contraception information sources for individuals with opioid use disorder
Bibliographic record
Abstract
OBJECTIVE (STUDY QUESTION): To identify trusted sources of contraception information among pregnancy-capable individuals with opioid use disorder (OUD). DATA SOURCES/STUDY SETTING: We conducted interviews between October 2018 and January 2019 at Boston Medical Center, a university-based tertiary care center. STUDY DESIGN: Data were drawn from semi-structured qualitative interviews with a convenience sample of 20 pregnant or recently pregnant individuals with OUD. We used the Ottawa Decision Support Framework, a health decision making conceptual model, to structure our interviews. We analyzed the data using inductive and deductive coding. DATA COLLECTION/ EXTRACTION METHODS: Not applicable. PRINCIPAL FINDINGS: Pregnancy-capable individuals who use opioids value friends who are not actively using opioids, including peers in recovery homes, as trusted sources of contraception information. They also value internet resources, including websites recommended by clinicians and social media posts, and established clinical providers as reliable sources of contraception information in ways that emulate individuals with other chronic medical conditions. CONCLUSION: These sources of contraception information may explain some trends in contraceptive use among individuals with OUD, inform nonstigmatizing contraceptive counseling, and serve as a foundation for improved decision support.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".