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Record W4400615122 · doi:10.22374/cjmrp.v19i1.46

Ontario Midwifery Clients' Experiences with the Management of Hyperbilirubinemia

2024· article· en· W4400615122 on OpenAlexfundaboutno aff
Faduma Gure, Tasha Macdonald, Alexa Minichiello, Sophia Kehler

Bibliographic record

VenueCanadian Journal of Midwifery Research and Practice · 2024
Typearticle
Languageen
FieldMedicine
TopicNeonatal Health and Biochemistry
Canadian institutionsnot available
FundersAssociation of Ontario MidwivesOntario Ministry of Health and Long-Term Care
KeywordsObstetricsNursingMedicinePsychology

Abstract

fetched live from OpenAlex

Hyperbilirubinemia is the most common reason for the hospitalization of a newborn within the first week of life. Very little is known, however, about the impact this diagnosis and subsequent management has on parents, particularly within the midwifery context. The Clinical Knowledge Translation department at the Association of Ontario Midwives conducted a multimethod qualitative research study in 2017 to better understand the experiences of midwifery clients whose newborns have been diagnosed with and required management of severe hyperbilirubinemia. Three focus-group discussions and one in-depth, in-person interview were conducted with midwifery clients in Ontario. Findings were analyzed using NVivo software, resulting in the extraction of key themes. Clients discussed how the transition from midwifery-led care to care being led by other health care providers in the hospital impacted them in the postpartum period. Clients also observed changes in the continuity of care provided by their midwives during management of hyperbilirubinemia, and discussed both the positive and negative impacts these changes had on their broader experience of the early postpartum period. These findings have informed the development of a series of knowledge translation tools that support midwives in providing optimal care to clients whose infants require phototherapy. This article has been peer reviewed.

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)0.002
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.075
GPT teacher head0.391
Teacher spread0.316 · 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 designNot applicable
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

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Midwifery Research and PracticeSame topicNeonatal Health and BiochemistryFrench-language works237,207