Women’s postpartum experiences in Canada during the COVID-19 pandemic: a qualitative study
Bibliographic record
Abstract
BACKGROUND: The mental health of postpartum women has worsened during the COVID-19 pandemic; however, the experiences that underlie this remain unexplored. The purpose of this study was to examine how people in Canada who gave birth during the pandemic were affected by policies aimed at limiting interpersonal contact to reduce SARS-CoV-2 transmission in hospital and during the early weeks postpartum. METHODS: We took a social constructionist approach and used a qualitative descriptive methodology. Sampling methods were purposive and involved a mix of convenience and snowball sampling via social media and email. Study inclusion was extended to anyone aged 18 years or more who was located in Canada and was pregnant or had given birth during the COVID-19 pandemic. Data were obtained via semistructured qualitative telephone interviews conducted between June 2020 and January 2021, and were analyzed through thematic analysis. RESULTS: Sixty-five interviews were conducted; data from 57 women who had already delivered were included in our analysis. We identified the following 4 themes: negative postpartum experience in hospital owing to the absence of a support person(s); poor postpartum mental health, especially in women with preexisting mental health conditions and those who had had medically complicated deliveries; asking for help despite public health regulations that prohibited doing so; and problems with breastfeeding owing to limited in-person follow-up care and lack of in-person breastfeeding support. INTERPRETATION: Policies that restrict the presence of support persons in hospital and at home during the postpartum period appear to be causing harm. Measures to mitigate the consequences of these policies could include encouraging pregnant people to plan for additional postpartum support, allowing a support person to remain for the entire hospital stay and offering additional breastfeeding support.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".