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Record W2889831492 · doi:10.23889/ijpds.v3i4.849

Evaluating the Manitoba Infant Feeding Database: Linking an infant feeding data repository with total population administrative data

2018· article· en· W2889831492 on OpenAlexaffabout
Julia Paul, Joanne Chateau, Chris Green, Lynne Warda, Alan Katz, Nathan Nickel

Bibliographic record

VenueInternational Journal for Population Data Science · 2018
Typearticle
Languageen
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsWinnipeg Regional Health AuthorityManitoba HealthUniversity of ManitobaPublic Health Agency of Canada
Fundersnot available
KeywordsBreastfeedingData collectionMedicineData qualityPopulationMedical recordDatabaseInfant mortalityInfant formulaPediatricsBreast feedingFamily medicineDemographyEnvironmental healthComputer scienceStatisticsBusinessMathematics

Abstract

fetched live from OpenAlex

IntroductionBreastfeeding during the first two years of life supports optimal maternal and child health. Few linkable databases capture infant feeding data beyond hospital discharge. The Manitoba Infant Feeding Database (MIFD), a novel initiative started in 2015, records infant feeding practices at vaccination visits and is linkable with administrative data.
 Objectives and ApproachOur objective was to evaluate the data quality and staff experiences with implementing the MIFD. The MIFD records whether the infant was exclusively or partially breastfeeding at each visit; it also records the infant’s age when (a) something other than human milk was first introduced and (b) the infant stopped breastfeeding entirely. Personal Health Identification Numbers (PHINs), birthdate, and postal code are used to link infant feeding information with administrative health records. Two authors independently reviewed the proportion of complete data fields and data fields with potential transcription errors. A survey was developed to assess experiences with implementing the MIFD.
 ResultsA total of 950 (out of 2500) records were randomly selected and reviewed, equating to 13,258 data fields. Data were 98.5% complete (n=13,064/13,258). Baby’s PHIN, mother’s PHIN, and relationship to the baby had 95.4%, 96.0%, and 97.6% complete data, respectively. Almost all records (95.5\%) had complete data for personal identifiers. Transcription had to be verified in 13.5% of MIFD data fields. The survey response rate was 78.4%. Nearly all felt that the MIFD data collection tool was easy to use (96.6\%). 65\% felt faxing the data to a central office was convenient. Most (93.1%) of respondents were happy to continue with the MIFD system. A 0.7 FTE is required to verify feeding data from all births in the province (N=15,000).
 Conclusion/ImplicationsThe MIFD is a sustainable and viable system for collecting and storing infant feeding information which can be linked with administrative health and social data. Having breastfeeding information after hospital discharge which can be linked with administrative data will facilitate the evaluation of programs aimed at supporting breastfeeding.

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)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0020.019
Open science0.0050.002
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 head0.279
GPT teacher head0.499
Teacher spread0.220 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2018
Admission routes2
Has abstractyes

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