Length of Postpartum Hospital Stays During COVID-19: Findings From a Convergent Parallel Mixed-Methods Study
Bibliographic record
Abstract
OBJECTIVES: We examined the length of postpartum hospitalization for live births during the COVID-19 pandemic and explored how pandemic circumstances influenced postpartum hospital experiences. METHODS: We conducted a cross-provincial, convergent parallel mixed-methods study in Ontario (ON) and British Columbia (BC), Canada. We included birthing persons (BPs) with an in-hospital birth in ON from 1 January to 31 March 2019, 2021, and 2022 (quantitative), and BPs (≥18 years) in ON or BC from 1 May 2020 to 1 December 2021 (qualitative). We linked multiple health administrative datasets at ICES and developed multivariable linear regression models to examine the length of hospital stay (quantitative). We conducted semi-structured interviews using qualitative descriptive to understand experiences of postpartum hospitalization (qualitative). Data integration occurred during design and interpretation. RESULTS: Relative to 2019, postpartum hospital stays decreased significantly by 3.29 hours (95% CI -3.58 to -2.99; 9.2% reduction) in 2021 and 3.89 hours (95% CI -4.17 to -3.60; 9.0% reduction) in 2022. After adjustment, factors associated with shortened stays included: giving birth during COVID-19, social deprivation (more ethnocultural diversity), midwifery care, multiparity, and lower newborn birth weight. Postpartum hospital experiences were impacted by risk perception of COVID-19 infection, clinical care and hospital services/amenities, visitor policies, and duration of stay. CONCLUSIONS: Length of postpartum hospital stays decreased during COVID-19, and qualitative findings described unmet needs for postpartum services. The integration of large administrative and interview data expanded our understanding of observed differences. Future research should investigate the impacts of shortened stays on health service outcomes and personal experiences.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".