An umbrella review on how digital health intervention co-design is conducted and described
Bibliographic record
Abstract
Co-design has been suggested to improve intervention effectiveness and sustainability. However, digital health intervention co-design is inconsistently reported. This umbrella review aims to synthesize what is known about co-design of digital health interventions. We searched five databases from inception. Reviews which reported on co-design methodologies used in digital health were eligible. Information on review type, health conditions, and reported specifics of co-design were extracted and synthesized. Methodological quality was assessed using the AMSTAR2 tool. We included 21 reviews published between 2015 and 2023. Co-design participants included patients, caregivers and healthcare professionals. The frequency and breadth of participant involvement in co-design activities were reported in less than half of reviews. Participants evaluated intervention co-design as a positive process. All reviews were rated as critically low quality. This umbrella review highlights the inconsistent reporting of co-design in digital health. Here, we emphasize the importance of creating guidelines to direct co-design activities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".