The Public’s views of mental health in pregnant and postpartum women: a population-based study
Bibliographic record
Abstract
BACKGROUND: We used population-based data to determine the public's views of prenatal and postnatal mental health and to identify predictors of those views. METHODS: A computer-assisted telephone survey was conducted by the Population Health Laboratory (University of Alberta) with a random sample of participants from the province of Alberta, Canada. Respondents were eligible to participate if they were: 1) ≥18 years; and 2) contacted by direct dialing. Questions were drawn from the Perinatal Depression Monitor, an Australian population-based survey on perinatal mental health; additional questions were developed and tested to reflect the Canadian context. Descriptive and multivariable regression analyses were conducted. RESULTS: Among the 1207 respondents, 74.7% had post-secondary education, 16.3% were in childbearing years, and over half (57.4%) reported knowing a woman who had experienced postpartum depression. Significantly more respondents had high levels of knowledge of postnatal (87.4%) than prenatal (70.5%) mental health (p < .01). Only 26.6% of respondents accurately identified that prenatal anxiety/depression could negatively impact child development. Personal knowledge of a woman with postpartum depression was a significant predictor of prenatal and postnatal mental health knowledge. CONCLUSIONS: While the public's knowledge of postnatal mental health is high, knowledge regarding prenatal mental health and its influence on child development is limited. Strategies for improving perinatal mental health literacy should target these knowledge deficits.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".