Assessment of Canadian perinatal mental health services from the perspective of providers: Where can we improve?
Bibliographic record
Abstract
Purpose: Perinatal mental health disorders are common, and rates have increased during the COVID-19 pandemic. It is unclear where providers may improve perinatal mental health care, particularly in countries lacking national guidelines, such as Canada. Methods: A cross-sectional survey of perinatal health providers was conducted to describe the landscape of perinatal mental health knowledge, screening, and treatment practices across Canada. Providers were recruited through listservs, social media, and snowball sampling. Participants completed an online survey that assessed their perinatal mental health training, service provision types, their patient wait times, and treatment barriers, and COVID-19 pandemic-related impacts. Results: A total of 435 providers completed the survey, including physicians, midwives, psychologists, social workers, nurses, and allied non-mental health professionals. Most (87.0%) did not have workplace mandated screening for perinatal mental illness but a third (66%) use a validated screening tool. Many (42%) providers stated their patients needed to wait more than 2 months for services. More than half (57.3%) reported they did not receive or were unsure if they received specialized training in perinatal mental health. Most (87.0%) indicated there were cultural, linguistic, and financial barriers to accessing services. Over two-thirds (69.0%) reported the COVID-19 pandemic reduced access to services. Conclusions: Survey findings reveal significant gaps in training, screening tool use, and timely and culturally safe treatment of perinatal mental health concerns. There is critical need for coordinated and nationally mandated perinatal mental health services in Canada to improve care for pregnant and postpartum people.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".