Mental Health Promotion and Stigma Reduction Among University Students Using the Reach, Efficacy, Adoption, Implementation, and Maintenance (RE-AIM) Framework: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Rapid urbanization, academic pressures, and developmental life transition stressors contribute to mental health stress for postsecondary students in China. Effective prevention, early identification, and timely intervention are challenged by stigma, a lack of mental health literacy, and inadequate mental health resources. OBJECTIVE: Our implementation science (IS) research project is aimed at evaluating the use of an evidence-informed mental health promotion intervention named Acceptance and Commitment to Empowerment - Linking Youth and 'Xin' (hearts) (ACE-LYNX) to promote university student mental health in Jinan, China. METHODS: We will engage and collaborate with Shandong Mental Health Center, the provincial mental health center, and six local universities in different regions of Jinan. The ACE-LYNX intervention aims to reduce social stigma against mental illness, enhance mental health literacy, and improve access to quality mental health care by increasing interdisciplinary collaboration and forming a mental health network. It is based on two evidence-based approaches, Acceptance and Commitment Therapy (ACT) and Group Empowerment Psychoeducation (GEP), and it will be delivered through online learning and in-person group training. The project will train 90 interdisciplinary professionals using the model. They will in turn train 15 professionals and 20 students at each university. The project will adopt the Reach, Efficacy, Adoption, Implementation, and Maintenance (RE-AIM) framework, which provides a structure to examine the process and outcomes of implementation using mixed methods comprising quantitative and qualitative approaches along five dimensions: reach, efficacy, adoption, implementation, and maintenance. RESULTS: Over the course of the project, 720 champions will be directly trained. They will contribute to developing a formal and informal mental health network, strengthened by student-led mental health initiatives and professional-led initiatives to promote collaborative care and facilitated care pathways. We anticipate that our project will reach out to 11,000 to 18,000 students. CONCLUSIONS: This IS protocol will outline our unique intervention model and key steps to contextualize, implement, and evaluate community-based mental health intervention. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/25592.
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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 | 0.063 | 0.039 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.033 | 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".