Maternal Psychological Distress & Mental Health Service Use during the COVID-19 Pandemic
Bibliographic record
Abstract
Background: Mental health problems are increasingly recognized as a significant and concerning secondary effect of the COVID-19 pandemic. Research on previous epidemics/pandemics suggest that families, particularly mothers, may be at increased risk, but this population has yet to be examined. The current study (1) described prevalence rates of maternal depressive and anxiety symptoms from an online convenience sample during the COVID-19 pandemic, (2) identified risk and protective factors for elevated symptoms, and (3) described current mental health service use and barriers. Methods: Participants (N = 641) were mothers of children age 0-8 years, including expectant mothers. Mothers completed an online survey assessing mental health, sociodemographic information, and COVID-19-related variables. Results: Clinically-relevant depression was indicated in 33.16%, 42.55%, and 43.37% of mothers of children age 0-18 months, 18 months to 4 years, and 5 to 8 years, respectively. Prevalence of anxiety was 36.27%, 32.62%, and 29.59% for mothers across age groups, respectively. Binary logistic regressions indicated significant associations between risk factors and depression/anxiety across child age groups. Limitations: Cross-sectional data was used to describe maternal mental health problems during COVID-19 limiting the ability to make inferences about the long-term impact of maternal depression and anxiety on family well-being. Conclusions: Maternal depression and anxiety appear to be elevated in the context of COVID-19 compared to previously reported population norms. Identified risk factors for depression and anxiety across different child age ranges can inform targeted early intervention strategies to prevent long-term impacts of the COVID-19 pandemic on family well-being and child development.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".