A rapid review for developing a co-design framework for a pediatric surgical communication application
Bibliographic record
Abstract
BACKGROUND: The exponential growth in the use of mobile health (mHealth) applications in patient care has highlighted the importance of understanding end-users' needs for successful interventions, achievable through the process of co-design. This review aims to summarize previously published uses of co-design in mHealth applications. It will inform the development of a local mHealth tool in pediatric surgery. METHODS: The rapid review was conducted following Cochrane Rapid Review guidelines. We searched four databases from inception until August 2021 for collaborative design in mHealth apps. Two reviewers independently screened titles and abstracts using Rayyan, with a third reviewer resolving any conflicts. Studies were excluded if they were non-empirical or in other languages than English. RESULTS: Out of 984 records screened, 175 were included for full-text screening, and 27 met all eligibility criteria. The purpose of the studies related to care support, communication, self-management or information management. All interventions involved their target audience in the co-design process, and 85% included healthcare professionals for expert opinion. Stakeholder participation was solicited via interviews, surveys, focus groups, design sessions, and prototype evaluation. To initiate the process, 15 studies included needs identification, 8 did background research, and 7 performed literature reviews. CONCLUSION: Co-design has a positive impact on mHealth development, research processes and outcomes. Its success relies on a clearly identified framework of terminology, activities involved, and evaluation techniques. The findings of this review will be integrated into a novel co-design framework, which will be used to inform the development of a pediatric surgery mHealth application. LEVEL OF EVIDENCE: This review uses manuscripts that are rated level III-V.
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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 | 0.107 | 0.221 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.037 | 0.025 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".