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Record W4417476623 · doi:10.24124/2025/30709

Providing newborn care in eat sleep console: A qualitative exploration of nurses’ experiences

2025· dissertation· W4417476623 on OpenAlexaboutno aff
Madison Friesen

Bibliographic record

Venuenot available
Typedissertation
Language
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Neonatal intensive care unitQualitative researchDyadFocus groupWorkloadHealth careNeonatal nursingGrounded theory

Abstract

fetched live from OpenAlex

,Perinatal substance use and neonatal withdrawal are increasing across Canada, creating significant challenges for families and the healthcare system. Traditional approaches to care, which rely heavily on pharmacological interventions, often result in prolo nged and costly admissions to neonatal intensive care units (NICUs). In response to these concerns, a team in the United States developed the Eat, Sleep, Console (ESC) model, which shifts the focus toward parental involvement and non-pharmacological care strategies rather than highly medicalized NICU care. In British Columbia, the ESC model has been adapted to emphasize trauma informed and culturally safe principles, placing greater responsibility on perinatal nurses who provide dyad care. Exploring nurses’ experiences of delivering newborn care within this model is essential to understanding the relational, clinical, and workload demands they face. Gaining insight into these perspectives is critical for supporting the sustainability of ESC in practice. An integrative literature review of 18 articles revealed that nurses’ experiences of caring for newborns with neonatal abstinence syndrome is complex and multifaceted. However, most of the existing evidence centers on NICU nurses and emphasizes traditional, pharmacological approaches. The perspectives of perinatal nurses, particularly those providing neonatal withdrawal care in settings outside the NICU, remain underexplored. To address this gap, this study set out to explore how perinatal nurses in Northern British Columbia experience providing newborn care in the context of ESC. Using Interpretive Description as the guiding methodology, semi-structured interviews were conducted with six perinatal nurses in one northern community. Data were analyzed thematically through an iterative coding process that began with structural coding aligned to the research questions, followed by pattern coding to identify broader themes across the dataset. The findings suggest that nurses experienced ESC care as complex and labour-intensive, requiring them to move well beyond the provision of direct newborn care. Their work encompassed fostering relationships with parents, providing extensive teaching to sup port parental independence, and managing the dynamics of team-based practice change. The overarching theme, The Work Really Isn’t About the Baby, reflected this emphasis, and was further articulated through three main themes: The Work Perinatal Nurses DO for ESC, TeamWORK in ESC, and the Work to embrace the change. Overall, the study highlights a persistent tension between the holistic intentions of ESC and the biomedical, task-oriented structures that continue to shape healthcare delivery. Nurses’ experiences illustrate the need for stronger organizational and structural supports to sustain trauma informed, culturally safe, and relational care, especially in the face of fluctuating patient volumes and entrenched systemic pressures. The clinical implications of these findings point to the importance of policies and staffing models that recognize the acuity of both newborns and their parents. Providing adequate capacity for nurses to deliver relational, family-centered care is essential. Strengthening interdisciplinary collaboration and supplementing limited social supports with dedicated roles can help reduce nursing burden and improve care for families affected by substance use disorders. Finally, organizational commitment to trauma informed and culturally safe practice is crucial for optimizing ESC implementation and fostering trust with families.

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)0.008
metaresearch head score (Gemma)0.011
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0100.009
Scholarly communication0.0040.003
Open science0.0020.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.369
Teacher spread0.341 · 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
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
Published2025
Admission routes1
Has abstractyes

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