Fit for purpose. Co-production of complex behavioural interventions. A practical guide and exemplar of co-producing a telehealth-delivered exercise intervention for people with stroke
Bibliographic record
Abstract
BACKGROUND: Careful development of interventions using principles of co-production is now recognized as an important step for clinical trial development, but practical guidance on how to do this in practice is lacking. This paper aims (1) provide practical guidance for researchers to co-produce interventions ready for clinical trial by describing the 4-stage process we followed, the challenges experienced and practical tips for researchers wanting to co-produce an intervention for a clinical trial; (2) describe, as an exemplar, the development of our intervention package. METHOD: We used an Integrated Knowledge Translation (IKT) approach to co-produce a telehealth-delivered exercise program for people with stroke. The 4-stage process comprised of (1) a start-up planning phase with the co-production team. (2) Content development with knowledge user informants. (3) Design of an intervention protocol. (4) Protocol refinement. RESULTS AND REFLECTIONS: The four stages of intervention development involved an 11-member co-production team and 32 knowledge user informants. Challenges faced included balancing conflicting demands of different knowledge user informant groups, achieving shared power and collaborative decision making, and optimising knowledge user input. Components incorporated into the telehealth-delivered exercise program through working with knowledge user informants included: increased training for intervention therapists; increased options to tailor the intervention to participant's needs and preferences; and re-naming of the program. Key practical tips include ways to minimise the power differential between researchers and consumers, and ensure adequate preparation of the co-production team. CONCLUSION: Careful planning and a structured process can facilitate co-production of complex interventions ready for clinical trial.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.035 |
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".