MétaCan
Menu
Back to cohort
Record W4403846814 · doi:10.1016/j.jnn.2024.10.006

Grow through what you go through: A qualitative description of South Asian immigrant mothers’ NICU experiences

2024· article· en· W4403846814 on OpenAlexafffund
Rosie Deol, Olive Wahoush, Ruth Chen, Michelle Butt

Bibliographic record

VenueJournal of Neonatal Nursing · 2024
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsImmigrationQualitative researchPsychologySociologyPolitical scienceAnthropologyLaw

Abstract

fetched live from OpenAlex

While existing research highlights the challenges mothers face in the Neonatal Intensive Care Unit, there is limited understanding of the specific experiences of South Asian immigrant mothers in this context. To describe and understand the experiences of South Asian immigrant mothers in the Neonatal Intensive Care Unit. Employed qualitative descriptive methodology, engaging four participants through semi-structured interviews and a demographic questionnaire. Data was analyzed via content analysis. Four key themes emerged: Seeking to Understand, The Impact of South Asian Culture on the Neonatal Intensive Care Unit Experience, Becoming a Mother One Step at a Time, and Circle of Care. South Asian immigrant mothers encounter numerous challenges in the Neonatal Intensive Care Unit, such as language barriers, societal perceptions, maternal self-doubt, and the complexities of navigating motherhood. These challenges highlight the importance of healthcare professionals such as nurses offering tailored and culturally-sensitive care to 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)0.006
metaresearch head score (Gemma)0.009
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 score0.012
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0110.008
Scholarly communication0.0030.003
Open science0.0020.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.064
GPT teacher head0.393
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

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Neonatal NursingSame topicMigration, Health and TraumaFrench-language works237,207