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Record W4303628702 · doi:10.3390/children9101517

Fostering Hope: Comprehensive Accessible Mother-Infant Dyad Care for Neonatal Abstinence (CAIN)

2022· article· en· W4303628702 on OpenAlexaffabout
Denise Clarke, Karen Foss, Natasha Lifeso, Matthew Hicks

Bibliographic record

VenueChildren · 2022
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsUniversity of AlbertaUniversity of CalgaryStollery Children's HospitalCovenant HealthAlberta Health Services
Fundersnot available
KeywordsThematic analysisQualitative researchNursingHealth careMedicineInclusion (mineral)Psychological interventionPsychologySocial psychologySociology

Abstract

fetched live from OpenAlex

Hospital and community healthcare providers have expressed concerns around the continuity and quality of care for infants with neonatal abstinence syndrome (NAS) during hospitalization and transition home. This qualitative study explored the experiences of hospital and community-based healthcare providers and identified themes related to the management of NAS for mothers and infants. Healthcare providers that cared for women with substance use disorders and/or cared for newborns with NAS in a large urban setting in Canada met inclusion criteria for this study and were interviewed in groups or as individuals. Interview transcripts were reviewed iteratively using inductive thematic analysis to identify an overarching theme linked with primary themes. In total, 45 healthcare providers were interviewed. Qualitative analysis of their experiences derived the overarching theme of hope with five primary themes being: mother/infant, mental health, system, judgement, and knowledge. The study identified gaps in NAS care including fear, stigma, and language. This research demonstrates that programs and interventions that work with mothers and newborns with NAS must foster hope in mothers, families, and in the extended care provider team and improve communication between hospital and community networks.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score0.842

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 science0.0000.001
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 head0.020
GPT teacher head0.284
Teacher spread0.264 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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