Conveying Missed Miscarriage Information at Obstetric Ultrasounds: Patient Experiences and Trauma-Informed Considerations
Bibliographic record
Abstract
Health system changes are needed to improve the care of people experiencing miscarriage, including patients receiving missed miscarriage information at obstetric ultrasounds. This study included policy research on prenatal care guidelines, policy research on employment legislation for bereavement leave, interviews with people who had lived/living experience with miscarriage recovery in British Columbia, Canada, and a dialogue with patients. Missed miscarriages are commonly diagnosed during routine obstetric ultrasounds, requiring ultrasound technicians and other healthcare providers to communicate missed miscarriage information to patients. Compassionate care and communication are needed to support patients and partners during this often-difficult time. Trauma-informed training should be provided to ultrasound technicians and other healthcare providers who communicate information on miscarriage to patients in clinics and hospitals, including emergency departments. Additionally, health systems can consider policy recommendations discussed in this article to place trauma considerations at the center of the patient/provider experience, such as allowing a support person to be present during the full duration of the obstetric ultrasound and inviting the patient to make the decision on receiving, or not receiving, the ultrasound picture.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".