CoDesign of a digital intervention for parents with bipolar disorder informed by integrated knowledge translation principles
Bibliographic record
Abstract
OBJECTIVES: To provide detailed information on the codesign of a digital intervention to support parents with bipolar disorder (BD) who have young children. Each step of this process is reported, as well as a detailed description of the final version of the intervention in line with the TIDieR framework. METHODS: Clinical experience and lived experience experts participated in online workshops, meetings, and remote feedback requests, informed by Integrated Knowledge Translation (IKT) principles. The IKT research group responded to each phase of recommendations from the knowledge users. RESULTS: Five clinical experience experts and six lived experience experts engaged with the codesign process. Their recommendations for principles, content, look, and feel, and functionality of the digital intervention were structured over five iterative phases. This led to a final implemented design that was identified by the clinical and lived experience experts (referred to together as the knowledge users group) as genuinely reflecting their input. CONCLUSIONS: The IKT principles offer an accessible structure for engaging with clinical and lived experience experts throughout a codesign process, in this case for a digital intervention for parents with BD. The resulting intervention is described in detail for transparency to aid further evaluation and development and to help other teams planning codesign approaches to intervention development.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".