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Record W4415287844 · doi:10.2196/72139

Nurse-Patient Communication During Postpartum Discharge Teaching: Protocol for a Mixed Methods Study

2025· article· en· W4415287844 on OpenAlexvenueno aff
Rebecca R. S. Clark, Patrina Sexton Topper, Tamar Klaiman, Rain Jacobson, Nadia Ngom, Naomi Kasahun, Kimberly De La Cruz, Celsea Tibbitt, Rebecca F. Hamm, Milisa Manojlovich

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldMedicine
TopicMaternal and fetal healthcare
Canadian institutionsnot available
FundersNational Institute of Nursing Research
KeywordsProtocol (science)Postpartum periodData collectionPregnancyMEDLINEPatient discharge

Abstract

fetched live from OpenAlex

BACKGROUND: Communication failures in inpatient maternity care are one of the leading causes of preventable maternal mortality. Most maternal mortality occurs during the postpartum period after hospital discharge. Nurses provide most direct inpatient maternity care and are responsible for postpartum discharge teaching, which is a critical moment for communicating about the care plan, concerns, warning signs, and follow-up plans to the patient, who will likely not be seen by a health care practitioner for 6 weeks, if at all. OBJECTIVE: The purpose of this study is to develop a deeper understanding of communication practices between nurses and first-time mothers during postpartum discharge teaching, including what supports or hinders the transfer of critical information and recommendations for improvement from the nurses and patients themselves. A secondary objective is to assess the acceptability, feasibility, and appropriateness of video-reflexive ethnography (VRE) as an intervention to improve care quality and processes. METHODS: We are using a health equity-informed mixed methods study design to develop a deeper understanding of communication practices between nurses and patients during postpartum discharge teaching for first-time mothers, including determinants for optimal communication and recommendations for improvement. Qualitative data will come from VRE, which will take place in 3 rounds: round 1 comprises video recording of actual postpartum discharge teaching, round 2 comprises independent review of the recording by both nurses and mothers, and round 3 comprises group reflexivity sessions with nurse participants. The planned analyses include a qualitative descriptive analysis of the video recordings and qualitative content analyses of the transcripts of the independent review and group reflexivity sessions. Quantitative data will come from a survey of nurse respondents regarding the feasibility, acceptability, and appropriateness of using VRE to reflect on and improve their practice. Survey results and reflections on VRE from round 3 will be integrated into a joint display. RESULTS: This project was funded in 2023 and approved by the Institutional Review Board of the University of Pennsylvania on December 6, 2023. Data collection will take place from 2024 to 2025. Results are expected to be published in 2026. CONCLUSIONS: Our work aims to engage with nurses and first-time mothers to identify opportunities to improve postpartum discharge teaching and communication. Secondarily, we plan to find out whether study participants find VRE feasible, acceptable, and appropriate for improving the quality of care and health care communication. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/72139.

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)0.110
metaresearch head score (Gemma)0.101
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 score0.110
Threshold uncertainty score0.580

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1100.101
Meta-epidemiology (narrow)0.0050.004
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0050.006
Science and technology studies0.0070.004
Scholarly communication0.0060.004
Open science0.0050.004
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0690.018

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 head0.257
GPT teacher head0.657
Teacher spread0.400 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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