Adjustment Disorder in Pregnant Women: Prevalence and Correlates in a Northern Mexican City
Bibliographic record
Abstract
BACKGROUND: The epidemiology of adjustment disorder in pregnant women is largely unknown. We sought to determine the prevalence and correlates of adjustment disorder in pregnant women in Durango City, Mexico. METHODS: Pregnant women (n = 300) attending in a public hospital in Durango City, Mexico were studied. All enrolled pregnant women had a psychiatric interview to evaluate the presence of adjustment disorder using the DSM-IV criteria. A questionnaire was submitted to obtain general epidemiological data of the pregnant women studied. Bivariate and multivariate analyses were used to assess the association of adjustment disorder with the epidemiological data of the women studied. RESULTS: Fifteen (5.0%) of the 300 women studied had adjustment disorder according to the DSM-IV criteria. Adjustment disorder was not associated with age, occupation, marital status, or education of pregnant women. In contrast, multivariate analysis of socio-demographic, clinical and psychosocial variables showed that adjustment disorder was associated with the variables lack of support from her couple (odds ratio (OR) = 3.83; 95% confidence interval (CI): 1.00 - 14.63; P = 0.04) and couple living abroad (OR = 10.12; 95% CI: 1.56 - 65.50; P = 0.01). CONCLUSIONS: This is the first report about the epidemiology of adjustment disorder in pregnant women in Mexico. Results provide evidence of the presence of adjustment disorder and contributing psychosocial factors associated with this disorder in pregnant women in Mexico. Results point towards further clinical and research attention should be given to this neglected disorder in pregnant women.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".