The impact of the COVID-19 pandemic on pregnancy-related healthcare : a systematic review of care practices and analysis of a local high-risk population
Bibliographic record
Abstract
The COVID-19 pandemic significantly disrupted healthcare delivery in Canada, including pregnancy-related services, due to public health restrictions and resource reallocation. Prenatal and postpartum care are essential for maternal and infant health, and interruptions may have lasting consequences, particularly for vulnerable populations. Recurrent pregnancy loss (RPL), affecting 1–3% of those trying to conceive, is a complex condition with physical, emotional, and psychological impacts. Effective management requires timely, individualized care, which may have been compromised during the pandemic. The first objective of this thesis was to assess the literature on how the pandemic affected pre- and postnatal care, including changes in service delivery, maternal mental health, and patient experiences, while identifying gaps related to RPL. A total of 168 studies were reviewed. Studies with qualitative components underwent thematic analysis, which revealed five key themes: (1) distress from altered or reduced quality of care; (2) challenges accessing information; (3) emotional impact of partner exclusion from care; (4) isolation during pregnancy and postpartum; and (5) worsened mental health and well-being. These findings underscore the significant disruptions to perinatal care during the pandemic and highlight the need to strengthen policies to better support vulnerable populations in future public health crises. The second objective was to evaluate trends in RPL healthcare utilization before and during the pandemic, with attention to the immediate impact of its onset and the role of socioeconomic factors. This retrospective cohort study analyzed patients at a tertiary referral centre, grouped by initial visit date: pre-pandemic (March 1, 2018–February 28, 2020) and pandemic (March 1, 2020–February 28, 2022). During the pandemic, total clinic visits increased, but initial visits declined. Virtual care expanded sharply, replacing many in-person appointments. Patients with multiple visits had shorter follow-up during the pandemic unless their care spanned both periods, in which case follow-up was longer. Visits dropped sharply at the onset of the pandemic but gradually recovered. Socioeconomic differences influenced access: patients from areas with higher material deprivation used fewer healthcare services, while those from areas with higher social deprivation used more.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.016 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".