DEPRESSIVE SYMPTOMS AND ASSOCIATED FACTORS IN PREGNANT WOMEN ATTENDED IN PRIMARY HEALTHCARE
Bibliographic record
Abstract
ABSTRACT Objective: to analyze the prevalence and factors associated with depressive symptoms in pregnant women attended in primary healthcare. Method: this is an epidemiological, cross-sectional and analytical study conducted in Montes Claros, in the north of the state of Minas Gerais, Brazil. The dependent variable (depressive symptoms) and independent variables (sociodemographic characteristics, social support, obstetric characteristics, sexuality and health conditions) were collected through a questionnaire and validated scales. The collection took place between October 2018 and November 2019. Descriptive, bivariate and multiple analyzes were performed through multinomial logistics regression. Results: a sample of 1,279 pregnant women was evaluated. The estimated prevalence of moderate and serious depressive symptoms was 16.2% and 25.2%, respectively. Low social support (p<0.001), low sexual performance (p = 0.002) and a high level of perceived stress (p<0.001) were factors associated with moderate depressive symptoms. First gestational trimester (p = 0.006), low social support (p<0.001), low sexual performance (p<0.001) and a high level of perceived stress (p<0.001) were factors associated with serious depressive symptoms. Conclusion: the prevalence of moderate and serious depressive symptoms in pregnant women attended in primary healthcare was considerable. Factors related to social support, gestational quarter (first quarter), sexuality and perceived stress showed association with these symptoms. Caution and the promotion of mental health is necessary for pregnant women in this scenario.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".