Who is distressed? A comparison of psychosocial stress in pregnancy across seven ethnicities
Bibliographic record
Abstract
BACKGROUND: Calgary, Alberta has the fourth highest immigrant population in Canada and ethnic minorities comprise 28 % of its total population. Previous studies have found correlations between minority status and poor pregnancy outcomes. One explanation for this phenomenon is that minority status increases the levels of stress experienced during pregnancy. The aim of the present study was to identify specific types of maternal psychosocial stress experienced by women of an ethnic minority (Asian, Arab, Other Asian, African, First Nations and Latin American). METHODS: A secondary analysis of variables that may contribute to maternal psychosocial stress was conducted using data from the All Our Babies prospective pregnancy cohort (N = 3,552) where questionnaires were completed at < 24 weeks of gestation and between 34 and 36 weeks of gestation. Questionnaires included standardized measures of perceived stress, anxiety, depression, physical and emotional health, and social support. Socio-demographic data included immigration status, language proficiency in English, ethnicity, age, and socio-economic status. RESULTS: Findings from this study indicate that women who identify with an ethnic minority were more likely to report symptoms of depression, anxiety, inadequate social support, and problems with emotional and physical health during pregnancy than women who identified with the White reference group. CONCLUSIONS: This study has identified that women of an ethic minority experience greater psychosocial stress in pregnancy compared to the White reference group.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".