Examining postpartum depression screening effectiveness in well child clinics in Alberta, Canada: A study using the All Our Families cohort and administrative data
Bibliographic record
Abstract
Affecting 10-15% of women, postpartum depression (PPD) can be debilitating and costly. While early identification has the potential to improve timely care, recommendations regarding the implementation of routine screening are inconsistent. In Alberta, screening is completed using the Edinburgh Postnatal Depression Scale during public health well child clinic visits. The objective of this study was to examine the effectiveness of screening in identifying, diagnosing and treating women at increased risk for PPD over the first year postpartum, compared to those unscreened. The All Our Families prospective pregnancy cohort was linked to public health, inpatient, outpatient, physician claims and community pharmaceutical data over the first year postpartum. Descriptive statistics and bivariate analyses examined differences in sample characteristics and PPD and non-PPD related utilization by screening category. Odds ratios and 95% confidence intervals for PPD diagnosis and mental health drugs dispensed were generated using crude and multivariable logistic regression models. Within our sample, 87% of the eligible population were screened, with 3% receiving a high-risk score, and 13% were unscreened. Compared to those unscreened, women screened high-risk had higher odds of being diagnosed with PPD (OR: 3.88, 95% CI: 2.18-6.92) and women screened low/moderate-risk had reduced odds of receiving a diagnosis (OR: 0.51, 95% CI: 0.35-0.74). High-risk women had an increased likelihood of diagnosis, higher PPD-related utilization and drugs dispensed compared to those unscreened. This information suggests that screening was effective at streamlining resources in Alberta. Future work should focus on evaluating the cost-effectiveness of PPD screening.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".