Virtual psychiatric care for perinatal depression (Virtual-PND): A pilot randomized controlled trial
Bibliographic record
Abstract
Barriers to in-person mental health care are common in pregnant and postpartum women with depression. We assessed the feasibility of a trial protocol for evaluating the use of secure, in-home synchronous virtual psychiatric care. In this pilot randomized controlled trial in Toronto, Canada, women aged ≥18 years, pregnant or 0-12 months postpartum, with Edinburgh Postnatal Depression Scale (EPDS) scores >12, were randomized 1:1 to in-person visits only, or to an intervention condition where they were offered the option of video-visits for some or all of their follow-up care. We assessed trial protocol feasibility, and secondarily EPDS score at 12 weeks post-randomization. 63 women were randomized (33 intervention, 30 control) of which 87.9% (n = 29) in the intervention group and 66.7% (n = 20) in control group completed the 12-week follow-up questionnaire. About 48.5% (n = 16) of intervention group participants used video-visits at least once, with high acceptability for participants and providers across a number of domains, and no adverse events. EPDS mean scores decreased from 16.6(SD 5.06) to 11.6(SD 4.77) and 16.9(SD 3.15) to 12.4(SD 3.96) for intervention and control groups, respectively (adjusted mean difference -0.64, 95%CI -2.95 to 1.67). It was feasible to recruit for a protocol evaluating psychiatrist video-visits for perinatal depression. Video-visits were acceptable to users and the psychiatrists providing their healthcare. A future non-inferiority efficacy trial can assess treatment outcome moderators to explore variability in effectiveness by illness severity and other factors, and cost-effectiveness of various types of video-visit strategies for psychiatric care in this population.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".