The Communities Organizing for Power Through Empathy (COPE) Community-Based Intervention to Improve Adult Mental Health During Disasters and Crises: Protocol for a Stepped-Wedge Cluster Randomized Trial
Bibliographic record
Abstract
BACKGROUND: Severe weather events, exacerbated by climate change, can lead to hardships such as displacement, resource scarcity, and social network disruptions. Such disasters impact mental health, triggering conditions such as anxiety, depression, and posttraumatic stress disorder. For communities in the Gulf South, the increasing frequency of disasters often further exacerbates already disastrous levels of inequality. In this context, there is an urgent need for evidence-based, multilevel, community-based interventions to support individual and community mental health and resilience. OBJECTIVE: This protocol describes the design of a community-based participatory research (CBPR) study to improve individual and community mental health in Gulf South communities by examining the implementation and effects of the multilevel, community-based intervention, Communities Organizing for Power through Empathy (COPE). Specifically, this study aims to (1) examine factors affecting the implementation, effectiveness, and adoption of the COPE intervention and (2) test its effects on mental health and community resilience. We hypothesize that participants in the COPE intervention will experience greater reductions in psychological distress (eg, perceived stress, anxiety, and depression); improvements in protective factors (eg, social support and coping self-efficacy); and community participation as compared with an attention control group. METHODS: The Consolidated Framework for Implementation Research guides our analysis of data collected from surveys, fidelity and field notes, interviews, and focus groups to examine aim 1. We examine aim 2 primarily via a 2-arm pragmatic stepped-wedge cluster randomized trial (SW-CRT) with individuals (approximately n=300) in clusters (faith-based and secular community-based organizations; approximately n=15) in a disaster-prone community in the US Gulf Coast. The cluster-based design implemented in steps supports the community-based nature of the study where timelines differ by organization. A total of 5 self-assessments will be conducted both in person and via email at later time points. We will integrate mixed methods in our analyses for aim 1 by combining themes from interviews and focus groups with implementation measures, and for aim 2 by constructing a data matrix to combine findings from the SW-CRT with thematic analyses of field notes and interviews. RESULTS: The SW-CRT is being conducted from June 2022 to June 2025. Recruitment began in April 2023, to conclude in spring 2025, to assess mental health, social support, and community resilience at 5 time points. Data analysis and dissemination of results are expected by spring of 2026. CONCLUSIONS: This protocol is among the first to use a CBPR approach to examine the implementation and effectiveness of a multilevel intervention on psychological distress and resilience. This study provides new insights into how CBPR can enhance intervention implementation research and expand the evidence on community-based mental health interventions during disasters. Policy makers should consider integrating CBPR approaches into disaster response frameworks to ensure culturally relevant and sustainable outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT06093737; https://clinicaltrials.gov/study/NCT06093737. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/63723.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.081 | 0.012 |
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".