A cross-sectional analysis of perinatal depressive symptoms among Punjabi-speaking women: are they at risk?
Bibliographic record
Abstract
BACKGROUND: Depression is the leading cause of disability for childbearing women. We examined three specific research questions among Punjabi-speaking women residing in the Fraser Health Authority: 1) What are the prevalence rates of prenatal depressive symptoms? 2) Do Punjabi-speaking women have a higher likelihood of reporting depressive symptoms compared to English-speaking women after controlling for age, level of education and financial worries, and 3) Given the same level of exposure to level of education and financial worries, do Punjabi-speaking women have the same likelihood of reporting depressive symptoms? METHODS: Data originated from the Fraser Health Authority prenatal registration database consisting of pregnant women (n = 9684) who completed a prenatal registration form between June 2009 and August 2010; 9.1 % indicated speaking Punjabi. The Whooley Depression Screen measured depressive symptoms. Chi-square tests and logistic multiple regression were used to examine the rates of reporting depressive symptoms among Punjabi-speaking women compared to English-speaking women. RESULTS: Punjabi-speaking women are at a higher risk for perinatal depressive symptoms. Women needing an interpreter were more likely to report prenatal depressive symptoms compared to English-speaking women. All registrants who reported financial worries had four and a half times the odds of reporting depressive symptoms. The impact of financial worries was significantly greater in the English-speaking women compared to the Punjabi-speaking women needing an interpreter. CONCLUSION: Using an established screening device, Punjabi-speaking women were found to be at higher risk for prenatal depressive symptoms.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".