Perinatal Mental Illness in Canada: Canadian Health Care Providers Do Not Feel Competent [ID: 1377412]
Bibliographic record
Abstract
INTRODUCTION: Consequences of untreated perinatal mental illness (PMI) can be severe and have negative implications for mothers and children across generations. Canadian data report 33% of new mothers are concerned about PMI, which is likely a very conservative estimate. Health care providers (HCPs) play a critical role in identification, screening, and treatment of PMI, yet we regularly receive requests for practice guidance. The purpose of this study was to explore practices, needs, and perceived competencies of HCPs related to PMI. METHODS: An online survey was administered to perinatal HCPs. Multiple choice questions and open text responses focused on needs, screening, treatment, referrals, and barriers. Descriptive statistics were used to report findings by profession, and text comments analyzed using content analysis. RESULTS: Respondents (N=290) frequently encounter patients with anxiety (84%), mood disorders (63%), and psychological aspects of transition to parenthood (60%). They occasionally encounter patients with eating (74%), personality (74%), or substance use (66%) disorders. Greater than 90% wanted more information about all disorders in the prenatal period; resources were most frequently requested; information about diagnosis was least requested. Family practitioners felt very confident screening for PMI (69%), but fewer were confident counselling (54%) or treating (27%) PMI. Few obstetricians (8%), nurses (15%), and midwives (16%) felt very confident providing counseling or treatment (1–4%). Barriers included a lack of awareness of supports and resources. CONCLUSION: Canadian HCPs do not feel competent to manage patients with PMI and need more guidance and resources. Results will inform development of targeted education and resource initiatives, including Clinical Practice Guidelines.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".