Including People With Lived Experience in Research From Design to Publication: The Next Steps for the IJIC Community
Bibliographic record
Abstract
In a recent article by Michgelsen et al. (2025) [1], a key insight emerged from a decade of research published in the International Journal of Integrated Care (IJIC): while the ambition to co-produce integrated care with people is widely stated, the reality often reflects a top-down approach of integrated care onto individuals.This discrepancy is not limited to practice and policy-it permeates the research landscape as well.The resulting gap risks producing research that does not respond to the users' needs and fails to generate the evidence required to create truly person-centred care.In this editorial, we aim to highlight the challenges and opportunities of co-producing research with people with lived experience, drawing on discussions and outcomes from a workshop held at ICIC25 and the longer journey IJIC has been on for the past 3 years.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.008 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".