A cross-sectional study of early identification of postpartum depression: Implications for primary care providers from The Ontario Mother & Infant Survey
Bibliographic record
Abstract
BACKGROUND: This survey's objective was to provide planning information by examining utilization patterns, health outcomes and costs associated with existing practices in the management of postpartum women and their infants. In particular, this paper looks at a subgroup of women who score >or= 12 on the Edinburgh Postnatal Depression Survey (EPDS). METHODS: The design is cross-sectional with follow-up at four weeks after postpartum hospital discharge. Five Ontario hospitals, chosen for their varied size, practice characteristics, and geographic location, provided the setting for the study. The subjects were 875 women who had uncomplicated vaginal deliveries of live singleton infants. The main outcome measures were the EPDS, the Duke UNC Functional Social Support Questionnaire and the Health and Social Services Utilization Questionnaire. RESULTS: EPDS scores of >or= 12 were found in 4.3 to 15.2% of otherwise healthy women. None of these women were being treated for postpartum depression. Best predictors of an EPDS score of >or= 12 were lack: of confident support, lack of affective support, household income of <20,000 dollars, wanting to stay in hospital longer, identification of learning needs while in hospital, self-identified care needs for an emotional/mental health problem that have not been met and mother's rating of own and baby's health as fair or poor. CONCLUSIONS: Primary care physicians, midwives, and public health nurses need to screen for depression at every opportunity early in the postpartum period. A mother's expression of undue concern about her own or her baby's health may be predictive of postpartum depression. Flexible, mother-focused support from community providers may decrease the prevalence of postpartum depression.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".