EMBED Phase 2 Study Protocol v.1.4: A Cluster Randomized Controlled Trial of Enhanced versus Standard Measurement-Based Care Implementation for Depression
Bibliographic record
Abstract
Background: Measurement-based care (MBC) is an evidence-based practice that incorporates routine outcome assessment using validated rating scales to guide collaborative clinical decision-making. Although MBC results in improved outcomes for patients with major depressive disorder (MDD), there are barriers to its broad implementation in clinical settings. The use of “enhanced” MBC (eMBC), with mobile apps that allow patients to track outcomes and engage in self-management via WeChat, may address some of these barriers. We hypothesize that implementation with eMBC using WeChat will be superior to standard MBC implementation using paper-pencil assessments at the clinic, for both implementation and clinical outcomes. Methods: We present a trial protocol for a 2-arm cluster randomized controlled trial (RCT) with a hybrid implementation-effectiveness design comparing standard MBC implementation versus eMBC implementation with 6-month follow up in 12 mental health centers in Shanghai, China. The eMBC implementation uses a WeChat mini-program that includes outcome tracking using brief questionnaires and self-management lessons supplemented with support by a lay coach via WeChat. Results: A total of 240 physicians and 1200 patients from the 12 mental health centers will be enrolled in the mixed-methods outcome analysis. The primary implementation outcome is implementation reach, defined as the proportion of eligible patients with a PHQ-9 score recorded in the hospital chart at 6 months after MBC implementation. The primary clinical outcome is clinical remission, defined as a PHQ-9 score of 4 or less at the 6-month follow up. Other implementation and clinical outcomes will be examined, including medication adherence, doctor-patient alliance, and a piggy-back cost-benefit economic analysis. Qualitative interviews will be conducted with physicians and patients to produce an interpretive account of the contextual factors which impact eMBC implementation.Conclusions: The results of this hybrid implementation-effectiveness cluster RCT will inform implementation of eMBC with WeChat mobile apps for patients with depression in other clinical settings in China and internationally.
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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 | 0.024 | 0.022 |
| Meta-epidemiology (narrow) | 0.009 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.042 | 0.007 |
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".