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Record W2987707817 · doi:10.1002/nop2.370

Experiences, mental well‐being and community‐based care needs of fathers of late preterm infants: A mixed‐methods pilot study

2019· article· en· W2987707817 on OpenAlexafffund
Shahirose Premji, Sandra M. Reilly, Genevieve Currie, Aliyah Dosani, Lynnette May Oliver, Abhay K. Lodha, Marilyn Young, Marc Hall, Tyler Williamson

Bibliographic record

VenueNursing Open · 2019
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsFoothills Medical CentreAlberta Health ServicesUniversity of CalgaryMount Royal UniversityYork University
FundersAlberta Centre for Child, Family and Community ResearchUniversity of Calgary
KeywordsPsychologyDevelopmental psychologyNursingMedicine

Abstract

fetched live from OpenAlex

Aims: We explore fathers' experience of caring for a late preterm infant including their stressors, needs and corresponding interventions proffered by public health nurses. Design: Pilot mixed-methods exploratory sequential design. Methods: 31) about fathers' levels of stress (Parenting Stress Index), anxiety (Speilberger State-Trait Anxiety) and depression (Edinburgh Postnatal Depression Scale) at 6-8 weeks after birth of their infant. Results: Fathers appreciated their infant was born 'early', however, discovered through experience the demands of their infant, which appeared as stress (child and parent domains) and anxiety. Themes: hypervigilance in care explained the fathers' sense of competency and role restriction; infant fatigue and parental feeding elucidated the stressful aspect of father-infant interaction. Unscientific advice from healthcare providers was confusing and frustrating while uncertainty of rehospitalization caused worries, fears or stress. One father experienced depressive symptoms.

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)0.003
metaresearch head score (Gemma)0.003
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.040
GPT teacher head0.369
Teacher spread0.329 · 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

Citations14
Published2019
Admission routes2
Has abstractyes

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