Characterizing worry content and impact in pregnant and postpartum women with anxiety disorders during COVID‐19
Bibliographic record
Abstract
The novel COVID pandemic has had a substantial impact on global mental health, including those populations that are inherently vulnerable such as pregnant and postpartum (perinatal) women. Anxiety disorders (ADs) are the most common mental health disorders during the perinatal period, affecting up to one in five women. However, since the onset of the pandemic, up to 60% of perinatal women are experiencing moderate to severe levels of anxiety. Given the substantial increase in perinatal anxiety during COVID, we sought to better understand its phenomenology by characterizing the collective worry content and impact of COVID using a content analysis. Eighty-four treatment-seeking pregnant (n = 35) and postpartum (n = 49) women with a principal AD, participated in this study between April and October 2020. In addition to completing questionnaire measures and a semistructured diagnostic interview, participants were asked to (1) describe their top excessive and uncontrollable worries, (2) describe additional COVID and non-COVID worries, and (3) describe how the pandemic had affected their lives. All responses were given verbally and transcribed verbatim by assessors. A content analysis led to the emergence of various COVID and non-COVID worry and impact themes. One third of participant's principal worries were specific to COVID, and 40% of COVID worries were specific to the perinatal context. Understanding the worry content and impact of COVID may improve symptom detection and inform the development of targeted treatment strategies to support the mental health needs of perinatal women with ADs throughout the pandemic and thereafter. Understanding pandemic-specific worries is important for perinatal symptom screening and may allow for the development of targeted treatment strategies to address COVID-specific worries and impact.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".