COVID-19 Public Health Restrictions and New Mothers’ Mental Health: A Qualitative Scoping Review
Bibliographic record
Abstract
Public health restrictions to protect physical health during the COVID-19 pandemic had unintended effects on mental health, which may have disproportionately affected some potentially vulnerable groups. This scoping review of qualitative research provides a narrative synthesis of new mothers’ perspectives on their mental health during COVID-19 pandemic restrictions through pregnancy to the postpartum period. Database searches in PubMed, CINAHL, and PsycINFO sought primary research studies published until February 2023, which focused on new mothers’ self-perceived mental health during the pandemic ( N = 55). Our synthesis found that new mothers’ mental health was impacted by general public health restrictions resulting in isolation from family and friends, a lack of community support, and impacts on the immediate family. However, public health restrictions specific to maternal and infant healthcare were most often found to negatively impact maternal mental health, namely, hospital policies prohibiting the presence of birthing partners and in-person care for their infants. This review of qualitative research adds depth to previous reviews that have solely examined the quantitative associations between COVID-19 public health restrictions and new mothers’ mental health. Here, our review demonstrates the array of adverse impacts of COVID-19 public health restrictions on new mothers’ mental health throughout pregnancy into the postpartum period, as reported by new mothers. These findings may be beneficial for policy makers in future public health emergency planning when evaluating the impacts and unintended consequences of public health restrictions on new mothers.
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 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 | 0.026 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".