Impact of level of personality pathology on affective, behavioral, and thought problems in pregnant women during the coronavirus disease 2019 pandemic.
Bibliographic record
Abstract
Among at-risk groups for psychological distress in the context of the Coronavirus Disease 2019 (COVID-19) pandemic, pregnant women might be especially vulnerable. Identifying subgroups of pregnant women at high risk of poor adaptation might optimize clinical screening and intervention, which could, in turn, contribute to mitigating the potentially devastating effects of prenatal stress on mothers and fetus. Level of personality functioning may be a good indicator of who may be more vulnerable to distress in challenging periods like the COVID-19 pandemic, as adults with high levels of personality dysfunction may experience significant difficulties in mentalizing threatening situations. The aims of the present study are (a) to determine the impact of level of personality pathology on affective, behavioral, and thought problems in pregnant women during the COVID-19 pandemic; and (b) to test a model where mentalization of trauma mediates the impact of personality pathology on symptomatology. Data from 1,207 French-Canadian pregnant women recruited through social media during the COVID-19 pandemic were analyzed. Latent profile analysis, using the Criterion A elements of the alternative model for personality disorders (Identity, Self-Direction, Empathy, Intimacy) as latent indicators, yielded four profiles: Healthy, Mild Self-Impairment, Intimacy Impairment, and Personality Disorder. Profiles showed significant associations with diverse indicators of symptomatology. Mediation models showed both direct and indirect (through mentalization of trauma) significant associations between level of personality functioning and affective/behavioral/thought problems. Results have clinical implications on prophylactic measures for at-risk pregnant women, especially in challenging contexts such as the COVID-19 pandemic. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".