Investigating the Impact of Online Abortion Myths on Healthcare Providers and Advocates: an Interview Study (Preprint)
Bibliographic record
Abstract
Background: Abortion is a common and safe medical intervention with a long history of practice in the United States. Despite this, inaccurate and misleading information persists, including falsehoods about the accessibility, legality, safety, and lived experience of abortion-related health care. Further, the prevalence and circulation of abortion myths online and offline have intensified following the overturning of Roe vs Wade in May 2022. While myths surrounding abortion have been widely documented, limited research has examined how these myths shape the everyday work of abortion health care providers and advocates, particularly in relation to patient-practitioner shared decision-making (SDM). Objective: This study examines how abortion myths affect abortion health care professionals-including practitioners in states where abortion access is legal, and advocates connecting patients with care in states with limited abortion health care. Through interviews with advocates and professionals, we document the nature of circulating myths, the common information sources where myths are being spread, and the routes professionals take to debunk myths when they arise, or, similarly, the barriers that limit their ability or desire to debunk abortion myths. Methods: We conducted in-depth qualitative interviews with abortion health care professionals across the WWAMI (Washington, Wyoming, Alaska, Montana, and Idaho) medical region. Interviews explored the types of abortion myths encountered in clinical settings, how providers respond to misinformation during patient interactions, and the perceived impact of myths on care delivery and SDM processes. Data were analyzed using an iterative constant comparative thematic analysis of the interview transcripts. Results: Participants reported encountering a wide range of abortion-related myths from predominantly online (social media) and some offline sources, including misinformation about safety, legality, fertility impacts, and procedural experiences. Providers described dedicating substantial time to correcting misinformation, addressing fear and confusion, and rebuilding trust within clinical encounters. Myths were found to complicate SDM by shaping patients' expectations, emotional responses, and perceived options for care, in addition to taking up time and resources for providers of care to counter damaging myths. Conclusions: Abortion myths have tangible effects on clinical practice and SDM, placing additional communicative and emotional labor on providers and potentially undermining patient autonomy. Addressing misinformation is therefore critical not only for public understanding but also for supporting equitable, patient-centered abortion care in a post-Roe landscape.
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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 | 0.014 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| 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".