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Record W3137592543 · doi:10.1177/1049732321994534

Reframing How Early Pregnancy Loss Is Viewed in the Emergency Department

2021· article· en· W3137592543 on OpenAlexaff
Katie N. Dainty, M. Bianca Seaton, Shelley McLeod, Modupe Tunde‐Byass, Elizabeth Tolhurst, Vanessa Rojas-Luengas, Darby Little, Catherine Varner

Bibliographic record

VenueQualitative Health Research · 2021
Typearticle
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsUniversity of British ColumbiaSinai Health SystemSchwartz/Reisman Emergency Medicine InstituteNorth York General HospitalUniversity of Toronto
Fundersnot available
KeywordsCognitive reframingEmergency departmentPregnancyPsychologyMedicineMedical emergencyObstetricsNursingSocial psychology

Abstract

fetched live from OpenAlex

Women experiencing early pregnancy loss frequently seek care in emergency departments or early pregnancy clinics. The existing qualitative literature on the experience of miscarriage has yet to address how to connect how these women perceive their care experience and the prevailing structures which may be at the root of why their experience continues to be challenging. This study aimed to look deeper into the sources of negative experiences of early pregnancy loss for insight into how to rethink where to make impactful changes to care. Phenomenologically informed interviews with 59 women revealed several points of tension in the framing of early pregnancy loss, including the view of miscarriage as common, of it as a medical versus emotional experience, and the assumptions around care needs. Our work suggests that these tensions need to be dismantled through more patient-centered approaches to patient-provider relationships, policies, models of care, and medical discourse.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.452
GPT teacher head0.612
Teacher spread0.160 · 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.

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

Citations21
Published2021
Admission routes1
Has abstractyes

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