Perspectives on Enhanced Measurement-Based Care Among Healthcare Providers, Adults, Adolescent Patients with Major Depressive Disorder and Pediatric Family Members: A Multicenter Online Investigation
Bibliographic record
Abstract
Haonan Zhang,1,* Ping Sun,2,* Xing Wang,1 Xiaorui Yang,3 Yuru He,1 Tao Yang,1 Yousong Su,1 Yi Fu,4 Qingwei Li,4 Jinhua Sun,5 Jing Liu,6 Jill K Murphy,6 Erin E Michalak,6 Raymond W Lam,6 Jun Chen,1 Yiru Fang1,7– 9 1Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China; 2Qingdao Mental Health Center, Qingdao, Shandong, People’s Republic of China; 3Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University, Shanghai, People’s Republic of China; 4Department of Psychiatry, Tongji Hospital, Tongji University School of Medicine, Shanghai, People’s Republic of China; 5Department of Psychiatry and Psychology, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, People’s Republic of China; 6University of British Columbia, Department of Psychiatry, Vancouver, British Columbia, Canada; 7Department of Psychiatry & Affective Disorders Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China; 8CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, People’s Republic of China; 9Shanghai Key Laboratory of Psychotic Disorders, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jun Chen, Clinical Research Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Xuhui District, Shanghai, 200030, People’s Republic of China, Email doctorcj2010@gmail.com Yiru Fang, Department of Psychiatry & Affective Disorders Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Huangpu District, Shanghai, 200025, People’s Republic of China, Email yirufang@aliyun.comObjective: Measurement-based care (MBC) is an emerging, objective, and systematic evidence-based practice for monitoring symptom severity and treatment efficacy to assist clinicians in developing individualized treatment strategies for patients with major depressive disorder (MDD). This study aimed to identify the barriers and facilitators of enhanced MBC (eMBC) in the outpatient setting to clarify the eMBC utilization dilemma.Methods: Between September 2022 and June 2023, we collected the opinions of healthcare providers, adult and adolescent patients, and family members of adolescent patients via online surveys. Specifically, we surveyed their acceptance and perspectives on MBC and eMBC primarily through custom-designed Likert scales developed for this study.Results: We received responses from 270 adult patients, 144 adolescent patients, 109 family members, and 355 healthcare providers. The results showed that 85.3% of patients and family members were willing to use the eMBC intervention. However, adolescent patients responded significantly differently from the other two groups, with lower acceptance and confidence. Among healthcare providers, while only 69.9% used MBC in practice, 94% believed standardized scales would be effective in treatment, and 91.8% were willing to try eMBC. Additionally, we received 277 remarks regarding eMBC from patients and families.Conclusion: In general, both clinicians and patients looked forward to using eMBC and recognized the potential benefits. However, they still had many concerns about privacy, professionalism, and time consumption. Responses from adolescent patients appeared more conservative and lacked confidence in eMBC. Further implementations are required to explore how eMBC can be operationalized in the outpatient setting to help different patients.Keywords: measurement-based care, enhanced measurement-based care, major depressive disorder, digital health, implementation, treatment
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".