Sociodemographic characterization of the population from the maternity ward of a differentiated perinatal hospital in Portugal
Bibliographic record
Abstract
Introduction: Recently in Portugal, several social and demographic changes have been observed with an emphasis on the increasing number of foreign citizens. The linguistic, cultural and social barriers cause challenges to maternal and child health care. This study aimed to characterize the sociodemographic and obstetrical data of mothers and newborns hospitalized in the maternity ward of a tertiary hospital in the southern region of Portugal. Methods: Cross-sectional observational study. A questionnaire was applied in the postpartum period to women admitted to the maternity ward of a tertiary hospital in Lisbon from 1 March 2021 to 30 June 2021. Results: 347 surveys were obtained from a total of 785 hospitalizations. The average maternal age was 31.8 years and the average paternal age was 33.7 years. Most mothers (79.1%) and fathers (77.3%) reported having, at least, secondary education. 19.5% of mothers and 7.7% of fathers were students or unemployed, 18.9% of parents were unqualified workers and 32.0% of mothers and 23.4% of fathers were professionals in the group of intellectual and scientific activities. More than a quarter of our population were migrants (27.6% of mothers and 26.2% of fathers) and, of these, 14.9% of mothers and 11.2% of fathers were not fluent in Portuguese. 7.9% of pregnancies did not meet the criteria for adequate prenatal care defined at the national level. There was an association (p < 0.01) between inadequate pregnancy surveillance and lower maternal age and lower educational level. There was no difference between the migrant and non-migrant population regarding the completion of the prenatal care program. Conclusions: The percentage of the migrant population is currently very expressive, with a non-negligible number of parents not fluent in Portuguese, which requires an adaptation of health resources to optimize the care provided. Lack of adequate prenatal care was expressive and was associated with lower maternal age and lower level of education.
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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 | 0.000 | 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) | 0.002 | 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".