Cumulative and compounding effects of pre-pandemic vulnerabilities and pandemic-related hardship on psychological distress among pregnant individuals
Bibliographic record
Abstract
Objective: Our primary objective was to determine whether pre-existing vulnerabilities and pandemic-related hardship resulted in cumulative (i.e., additive) versus compounding (i.e., multiplicative) effects on psychological distress in pregnant individuals during the COVID-19 pandemic. A secondary objective was to determine whether perceived social support and/or receipt of government financial aid buffered the effects of pandemic-related hardship on psychological distress.Method: Data are from a prospective pregnancy cohort study, the Pregnancy During the COVID-19 Pandemic study (PdP). This cross-sectional report is based upon the initial survey collected at recruitment between April 5, 2020 and April 30, 2021. Logistic regression was used to evaluate our objectives.Results: The data provide substantial evidence supporting a cumulative (additive) relationship between pandemic-related hardship and pre-existing vulnerabilities in relation to psychological distress. There was no evidence to support compounding (multiplicative) effects. Likewise, the buffering effect of social support was additive but not multiplicative. Government financial aid did not buffer the effects of objective hardship on psychological distress. Conclusion: Pre-pandemic vulnerability and pandemic-related hardship had cumulative effects on psychological distress during the COVID-19 pandemic. Adequate and equitable responses to pandemics and disasters may require more intensive supports for those with multiple vulnerabilities.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".