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Record W3153747942 · doi:10.2196/27382

Development and Implementation of a Mobile Tool for High-Risk Pregnant Women to Deliver Effective Caregiving for Neonatal Abstinence Syndrome: Protocol for a Mixed Methods Study

2021· article· en· W3153747942 on OpenAlexvenueno aff
Ekaterina Burduli, Hendrée E. Jones, Olivia Brooks, Celestina Barbosa‐Leiker, Ron Kim Johnson, John M. Roll, Sterling McPherson

Bibliographic record

VenueJMIR Research Protocols · 2021
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsnot available
FundersNational Institute on Drug Abuse
KeywordsMedicineUsabilityPsychological interventionRandomized controlled trialPregnancyNursingFamily medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The United States continues to experience an alarming rise in opioid use that includes women who become pregnant and related neonatal abstinence syndrome (NAS) in newborns. Most newborns experiencing NAS require nonpharmacological care, which entails, most importantly, maternal involvement with the newborn. To facilitate positive maternal-newborn interactions, mothers need to learn effective caregiving NAS strategies when they are pregnant; however, an enormous gap exists in the early education of mothers on the symptoms and progression of NAS, partly because no education, training, or other interventions exist to prepare future mothers for the challenges of caring for their newborns at risk for NAS. OBJECTIVE: In this paper, we describe a mixed methods, multistage study to adapt an existing mobile NAS tool for high-risk pregnant women and assess its usability, acceptability, and feasibility in a small randomized controlled trial. METHODS: Stage 1 will include 20 semistructured interviews with a panel of neonatology experts, NAS care providers, and mothers with experience caring for NAS-affected newborns to gather their recommendations on the management of NAS and explore their perspectives on the care of these newborns. The findings will guide the adaptation of existing mobile NAS tools for high-risk pregnant women. In stage 2, we will test the usability, acceptability, and feasibility of the adapted mobile tool via surveys with 10 pregnant women receiving opioid agonist therapy (OAT). Finally, in stage 3, we will randomize 30 high-risk pregnant women receiving OAT to either receive the adapted mobile NAS caregiving tool or usual care. We will compare these women on primary outcomes-maternal drug relapse and OAT continuation-and secondary outcomes-maternal-newborn bonding; length of newborn hospital stays; readmission rates; breastfeeding initiation and duration; and postpartum depression and anxiety at 4, 8, and 12 weeks postpartum. RESULTS: This project was funded in July 2020 and approved by the institutional review board in April 2020. Data collection for stage 1 began in December 2020, and as of January 2021, we completed 18 semistructured interviews (10 with NAS providers and 8 with perinatal women receiving OAT). Common themes from all interviews will be analyzed in spring 2021 to inform the adaptation of the NAS caregiving tool. The results from stage 1 are expected to be published in summer 2021. Stage 2 data collection will commence in fall 2021. CONCLUSIONS: The findings of this study have the potential to improve NAS care and maternal-newborn outcomes and lead to commercialized product development. If effective, our new tool will be well suited to tailoring for other high-risk perinatal women with substance use disorders. TRIAL REGISTRATION: ClinicalTrials.gov NCT04783558; https://clinicaltrials.gov/ct2/show/NCT04783558. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/27382.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.055
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.055
Threshold uncertainty score0.292

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0550.044
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0030.003
Science and technology studies0.0050.003
Scholarly communication0.0040.003
Open science0.0050.004
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0440.008

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 head0.078
GPT teacher head0.537
Teacher spread0.459 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreProtocol

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

Citations3
Published2021
Admission routes1
Has abstractyes

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