Trends in very early discharge from hospital for newborns under midwifery care in Ontario from 2003 to 2017: a retrospective cohort study
Bibliographic record
Abstract
Background: Very early discharge from hospital is an element of Ontario midwifery care. Our aim in the present study was to describe the frequency of very early hospital discharge for newborns in Ontario midwifery care over time. Methods: We conducted a retrospective population-based cohort study, including all midwife-attended singleton term cephalic newborns delivered by spontaneous vaginal birth at Ontario hospitals between April 2003 and February 2017. Our primary outcome was very early hospital discharge (< 6 h after birth) for newborns. Secondary outcomes were pediatric consultation before hospital discharge, phototherapy before hospital discharge and readmission for treatment of jaundice. We used generalized linear mixed models to estimate the relation between maternal, neonatal and hospital factors and very early discharge, while accounting for clustering by hospital. Results: The study cohort included 101 852 newborns born at 89 hospitals. Between 2003/04 and 2016/17, the unadjusted rate of very early discharge decreased from 34.3% to 30.7%. This trend was not significant after adjustment for covariates (odds ratio 1.0, 95% confidence interval 0.99–1.0). Unadjusted rates of pediatric consultation, phototherapy and readmission for jaundice all rose slightly over the study period. Hospital-specific risk-adjusted frequencies of very early discharge ranged from 5% (n = 1479) to 83% (n = 3459) across the 75 Ontario hospitals with at least 100 newborns included in the study cohort. Interpretation: Hospital-level factors contributed to the observed decrease in crude rates of very early discharge for midwifery clients. Wide variation in these rates across Ontario hospitals points to room for improvement to make more efficient use of health care resources by promoting optimal levels of very early discharge.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".