Quality assurance assessment of a specialized perinatal mental health clinic
Bibliographic record
Abstract
BACKGROUND: Mood and anxiety issues are the main mental health complaints of women during pregnancy and the postpartum period. Services targeting such women can reduce perinatal complications related to psychiatric difficulties. This quality assurance project aimed to examine changes in mood and anxiety symptoms in pregnant and postpartum women referred to the Women's Health Concerns Clinic (WHCC), a specialized outpatient women's mental health program. METHODS: We extracted patient characteristics and service utilization from electronic medical records of women referred between 2015 and 2016. We also extracted admission and discharge scores on the Edinburgh Postnatal Depression Scale (EPDS) and the Generalized Anxiety Disorder-7 (GAD-7) scale. RESULTS: Most patients accessed the WHCC during pregnancy (54%), had a diagnosis of major depressive disorder (54.9%), were prescribed a change in their medication or dose (61.9%), and accessed psychotherapy for perinatal anxiety (30.1%). There was a significant decrease in EPDS scores between admission and discharge (t(214) = 11.57; p = .000; effect size d = .86), as well as in GAD-7 scores (t(51) = 3.63; p = .001; effect size d = .61). A secondary analysis showed that patients with more severe depression and anxiety symptoms demonstrated even greater effect sizes. CONCLUSIONS: Changes in EPDS and GAD-7 scores indicate that the WHCC is effective in reducing mood and anxiety symptoms associated with the perinatal period. This project highlights the importance of quality assurance methods in evaluating the effectiveness of clinical services targeting perinatal mental health, in order to inform policy and funding strategies.
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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 | 0.029 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".