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Record W4385979639 · doi:10.3390/children10081407

Butterflies and Ribbons: Supporting Families Experiencing Perinatal Loss in Multiple Gestation

2023· article· en· W4385979639 on OpenAlexaffabout
Béatrice Boutillier, Nicholas D. Embleton, Sophie Bélanger, Alexie Bigras-Mercier, Audrey Larone Juneau, Keith J. Barrington, Annie Janvier

Bibliographic record

VenueChildren · 2023
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsAuditGratitudePsychologyMedicineFeelingSurpriseToddlerFamily medicinePediatricsDevelopmental psychologyNursingSocial psychology

Abstract

fetched live from OpenAlex

Introduction: In neonatology, multiple pregnancies are common. Unfortunately, it is not rare for one baby to die. Communication with parents in these circumstances has been demonstrated to be sub-optimal. Methods: Two educational programs were evaluated with pre- and post-course surveys, questionnaires administered to participants, and audits. Results: In the online Butterfly project (UK; n = 734 participants), all participants reported that the training exceeded or met their expectations, 97% reported they learned new skills, and 48% had already applied them. Participants expressed gratitude in their open-ended answers: “I feel a lot more confident in supporting parents in this situation”. In the Ribbon project (workshop for neonatal clinicians, Quebec; n = 242), 97% were satisfied with the training and reported feeling more comfortable caring for bereaved parents. Knowledge improved pre–post training. Audits revealed that 100% of cases were identified on the incubator and the baby’s/babies’ admission card, all changed rooms after the death of their co-twin/triplet, and all had the name of their co-twin/triplet on the discharge summary. All clinicians (55) knew what the ribbon symbol meant when asked during surprise audits at the bedside. Conclusion: Different educational strategies to optimize communication with families after the perinatal loss of a co-twin are appreciated and have a positive impact.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.455

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 communication0.0000.000
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 head0.026
GPT teacher head0.332
Teacher spread0.306 · 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 designObservational
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

Citations4
Published2023
Admission routes2
Has abstractyes

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