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Record W4410743114 · doi:10.1177/23743735251343497

Conveying Missed Miscarriage Information at Obstetric Ultrasounds: Patient Experiences and Trauma-Informed Considerations

2025· article· en· W4410743114 on OpenAlexaffabout
Rana Van Tuyl, Kathryn Berry-Einarson

Bibliographic record

VenueJournal of Patient Experience · 2025
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsProvincial Health Services AuthorityRoyal Roads University
Fundersnot available
KeywordsMiscarriageMedicineHealth careMedical emergencyLegislationNursingObstetricsPregnancyFamily medicine

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.331
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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