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Record W4226344379 · doi:10.1097/jpn.0000000000000597

Care Experiences of Persons With Perinatal Opioid Use

2021· article· en· W4226344379 on OpenAlexaff
Lisa M. Blair, Kristin Ashford, Lauren Gentry, Sarah Bell, Amanda Fallin‐Bennett

Bibliographic record

VenueThe Journal of Perinatal & Neonatal Nursing · 2021
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsBell (Canada)
Fundersnot available
KeywordsOpioid use disorderMedicineQualitative researchNursingPsychiatryFamily medicinePsychologyOpioid

Abstract

fetched live from OpenAlex

Opioid use in the perinatal period has escalated rapidly, with potentially devastating outcomes for perinatal persons and infants. Substance use treatment is effective and has the potential to greatly improve clinical outcomes; however, characteristics of care received from providers including nurses have been described as a barrier to treatment. Our purpose was to describe supportive perinatal care experiences of persons with opioid use disorder. A qualitative descriptive study design was used to examine experiences of 11 postpartum persons (ages 22-36 years) in medication-assisted treatment for opioid use disorder at an academic medical center in the southern region of the United States. Participants were interviewed about experiences with perinatal and neonatal care during the child's hospitalization for neonatal abstinence syndrome surveillance and/or treatment. Four themes of supportive care experiences emerged: informing, relating, accepting, and holistic supporting. Participants reported a range of positive and negative perinatal care experiences, with examples and counterexamples provided. This fuller understanding of perceptions and lived experiences of care can inform practice changes and educational/training priorities. Future research is needed to facilitate development of comprehensive care models geared to address perinatal care needs of persons with opioid use disorder.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.695

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.012
GPT teacher head0.268
Teacher spread0.257 · 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

Citations13
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Perinatal & Neonatal NursingSame topicPrenatal Substance Exposure EffectsFrench-language works237,207