Scaling Up Maternal Mental healthcare by Increasing access to Treatment (SUMMIT): Precision Analysis Protocol
Bibliographic record
Abstract
The overarching goal of the Scaling Up Maternal Mental healthcare by Increasing access to Treatment (SUMMIT) trial is to examine the scalability of patient-centered provision of brief, evidence-based psychological treatments for perinatal depression and anxiety (N=1,226). Specifically, and through a multi-site, randomized, non-inferiority trial, the trial examines whether a brief, behavioral activation (BA) treatment delivered via telemedicine is as effective as the same treatment delivered in-person; and whether BA delivered by non-specialist providers (nurses, midwives, etc. with no previous mental health training) with appropriate training is as effective as when delivered by specialist providers (psychiatrists, psychologists and social workers) in reducing perinatal depressive and anxiety symptoms. The study is being conducted in Toronto, Chicago and Chapel Hill. The trial will also identify relevant underlying implementation processes and determine whether, and to what extent, these strategies work differentially for certain women compared to others. The primary objectives of the larger trial are to: • Examine if a brief, BA psychological treatment delivered by non-specialist providers (NSP) is as effective in treating perinatal depressive symptoms as specialist-delivered treatment* (Primary Aim 1); and • Examine if a brief BA psychological treatment delivered through telemedicine (TM) is as effective in treating perinatal depressive symptoms as in-person (IP; Primary Aim 2)*. *Note: ‘as effective’ is the language that we used in the PCORI submission. After consultations with several statistical experts, we will cater the language accordingly for the audience e.g., use ‘non-inferior’ for academic audiences and the current language for lay audiences. The secondary objective that applies to the Process Analysis described in this preregistration is to conduct a process evaluation, i.e., identify the underlying processes related to delivery and scalability of a brief psychological treatment for perinatal depressive and anxiety symptoms from a multi-stakeholder perspective.
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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 | 0.069 | 0.117 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.121 | 0.019 |
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".