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Record W4283019448 · doi:10.1177/23333936221097113

Alberta Family Integrated Care™ and Standard Care: A Qualitative Study of Mothers’ Experiences of their Journeying to Home from the Neonatal Intensive Care Unit

2022· article· en· W4283019448 on OpenAlexafffundabout
Rachael Dien, Karen Benzies, Pilar Zanoni, Jana Kurilova

Bibliographic record

VenueGlobal Qualitative Nursing Research · 2022
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsUniversity of Calgary
FundersAlberta Innovates - Health Solutions
KeywordsNeonatal intensive care unitNursingGrounded theoryQualitative researchIntensive careContext (archaeology)MedicineHealth carePsychologyDevelopmental psychologyPediatricsIntensive care medicineSociology

Abstract

fetched live from OpenAlex

Globally, one in ten infants is born preterm. Most preterm infants require care in a level II Neonatal Intensive Care Unit (NICU), which are highly technological critical care environments that can be overwhelming for parents. Alberta Family Integrated Care (AB-FICare™) is an approach to care that provides strategies to integrate parents into their infant’s care team. This sub-study is the first to compare mothers’ experiences in the context of AB-FICare™ and standard care. Semi-structured interviews with mothers from AB-FICare™ ( n = 14) and standard care ( n = 12) NICUs were analyzed using interpretive description informed by grounded theory methods. We identified a major theme of Journeying to Home with six categories: Recovering from Birth, Adapting to the NICU, Caring for Baby, Coping with Daily Disruption, Seeing Progress, and Supporting Parenting. Mothers in the AB-FICare™ group identified an enhancement to standard care related to building reciprocal trust with healthcare providers that accelerated Journeying to Home.

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)0.004
metaresearch head score (Gemma)0.006
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 score0.799
Threshold uncertainty score0.399

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0100.007
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.098
GPT teacher head0.466
Teacher spread0.368 · 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

Citations21
Published2022
Admission routes3
Has abstractyes

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