Identifying women at‐risk for postpartum depression in the immediate postpartum period
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To develop a multifactorial predictive model of depressive symptomatology in the first week postpartum in order to assist in targeted screening procedures. METHOD: As part of a longitudinal study, a population-based sample of 594 mothers in a health region near Vancouver, British Columbia completed a mailed questionnaire at 1-week postpartum that included diverse risk factors from the following domains: sociodemographic, biological, pregnancy-related, life stressors, social support, obstetric and adjustment to motherhood. Following univariate analysis, sequential regression analysis was completed to develop a multifactorial predictive model. RESULTS: In the multivariate model, the factors predictive of depressive symptomatology at 1-week postpartum included immigration within the last 5 years, history of depression independent of childbirth, diagnosis of pregnancy-induced hypertension, vulnerable personality style, stressful life events, lack of perceived support, lack of readiness for hospital discharge and dissatisfaction with infant feeding method. CONCLUSION: The findings suggest that several risk factors for depressive symptomatology in the immediate postpartum period are consistent with previously identified factors but other factors such as recent immigrant status, feeling unready for hospital discharge, dissatisfaction with their infant feeding method, and pregnancy-induced hypertension should also be examined.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it