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Record W6989434972

Assessing the associations between selected screening variables from the Healthy Babies Healthy Children (HBHC) screening tool in relation to mortality, hospitalizations and emergency room visits among infants and children

2025· dissertation· en· W6989434972 on OpenAlexaboutno aff

Bibliographic record

VenueQSpace (Queen's University Library) · 2025
Typedissertation
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
Fundersnot available
KeywordsPublic healthCohort studyRisk assessmentInfant mortalityLongitudinal studyRetrospective cohort studyCohortPostpartum period
DOInot available

Abstract

fetched live from OpenAlex

Background. Identifying infant and child risk, monitoring outcomes, and implementing supportive community programs are significant public health priorities. Screening for risk during the early postpartum period is optimal, and Canadian public health programs include a variety of screening tools and processes. Aim. This research aimed to inform the Ontario Healthy Babies Healthy Children (HBHC) screening process for identifying infants and children who may be at potential risk of mortality, hospital admissions, and emergency room visits. Methods. We conducted a retrospective longitudinal cohort study that included all women and their infants, meeting study criteria, who were born in 2013 in Ontario (N = 128,875). Using administrative data housed in ICES, 18 variables representing responses from the HBHC Screening Tool were evaluated to determine associations with the study outcomes. Associations with the outcomes were measured for infants from birth to 1 year and children from 1- 6 years. Sensitivity and specificity testing were completed to determine optimal cutoff points for identifying risk mortality and hospital admissions. Results. We demonstrated that the risk of infant mortality was associated with a cutoff point of 4 and 18 variables representing the HBHC screening responses. We further demonstrated that a reduced 9-variable model was equally as sensitive to establishing the risk of infant mortality using a cutoff point of 2. Other tested infant and child models were not as robust or sensitive, suggesting that additional variables influence the tested outcomes. Contribution. This study contributes to an existing body of knowledge that can inform the current HBHC screening protocol and process. Early postpartum screening is an initial step in risk identification. Further validation of tools and processes is warranted.

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)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.848
Threshold uncertainty score0.305

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.010
GPT teacher head0.257
Teacher spread0.246 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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