Self-Management Support After Burns: Protocol for a Multicenter, Stepped-Wedge Hybrid Type II Effectiveness-Implementation Study
Bibliographic record
Abstract
Background: After a burn injury, the survivors have to manage and integrate the physical, psychological, and social consequences of their injury into their daily lives, such as functional limitations, aesthetic complaints, and fatigue. How successful survivors of burn injuries are at this depends on their self-management skills. Health care professionals play an important role in supporting the self-management of survivors of burn injuries. Currently, there are no burn-specific self-management support interventions. Therefore, we developed a self-management support intervention for survivors of burn injuries, called BreeZe (Brandwonden en Zelfmanagement). Objective: This study aimed to describe a study protocol to implement and evaluate the BreeZe intervention. Methods: This multicenter study in the Netherlands is an implementation-effectiveness hybrid type 2 study, with a nonrandomized stepped-wedge design. Starting April 2024, 3 phases have been sequentially rolled out across the 3 specialized Dutch burn centers over a period of 20 weeks-the preimplementation phase (usual care), implementation phase, and postimplementation phase. To identify barriers and facilitators of implementation, the Consolidated Framework for Implementation Research (CFIR) will be used. For evaluation, the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) evaluation framework is used. The coprimary outcomes are (1) self-management skills and (2) the implementation outcomes are reach, adoption, implementation, and maintenance. Secondary effectiveness outcomes are self-regulation, participation, dependency, patient-centeredness for survivors of burn injuries, self-management support skills for health care professionals, and cost-effectiveness. Data collection for survivors of burn injuries occurs at 2 weeks, 6 months, and 12 months post discharge, using questionnaires. Data collection for health care professionals occurs before training and 3, 6, and 12 months post implementation, using questionnaires, video observations, and interviews. Data analysis will include both quantitative and qualitative methods for comprehensive evaluation. Results: Participant recruitment ended on June 30, 2025. Follow-up data collection ends in July 2026. Conclusions: This study will evaluate both the effectiveness and implementation of the BreeZe self-management support intervention for survivors of burn injuries using a hybrid effectiveness-implementation design. By applying the CFIR and RE-AIM frameworks within a stepped-wedge design embedded in routine burn aftercare, this study aims to generate robust and practice-relevant evidence on how self-management support can be effectively implemented in burn care. The findings are expected to inform both clinical practice and future implementation efforts in burn aftercare settings.
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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 | 0.053 | 0.028 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.058 | 0.008 |
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".