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Financial costs for parents with a baby in a neonatal nursery

2009· article· en· W2093435503 on OpenAlexaffabout
Brenda Argus, Jennifer A. Dawson, Connie Wong, Colin J. Morley, Peter G. Davis

Bibliographic record

VenueJournal of Paediatrics and Child Health · 2009
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsRoyal Ottawa Mental Health Centre
FundersNational Medical Research CouncilNational Health and Medical Research Council
KeywordsMedicinePediatricsNeonatal intensive care unitQuarter (Canadian coin)DemographyFinanceFamily medicine

Abstract

fetched live from OpenAlex

AIM: To determine the additional financial cost to families of babies admitted to the nurseries of The Royal Women's Hospital, Melbourne, Australia. METHODS: Prospective case series of consecutive babies admitted to the Special and Intensive Care Nurseries at The Royal Women's Hospital, Melbourne, Australia. Data were collected from diaries completed by parents who recorded expenses related to having their baby in hospital. Fifty nine families of babies born <34 weeks' gestation who were hospitalised for at least 2 weeks. RESULTS: The median expenditure per family per week was Australian (A) $243 and the median length of stay in the nurseries was 7 weeks. The major costs were related to food and transport. Expenses related to the expression/storage of breast milk and accommodation were also considerable consuming 11% and 14%, respectively of the weekly amount spent. Of the 23 families who reported lost or reduced income, the median amount lost per week per family was A$324. CONCLUSION: The financial burden on families with babies admitted to a tertiary neonatal unit is substantial. The median cost per week was approximately one quarter of the average gross weekly income and included lost income as well as additional expenses. It is important that institutions and health-care systems recognise the magnitude of this additional burden on vulnerable 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.268
Teacher spread0.261 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations24
Published2009
Admission routes2
Has abstractyes

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