Co-producing data-intensive research with an underserved group: a case study and evaluation identifying pathways to impact
Bibliographic record
Abstract
Introduction: Co-production of research, where researchers and experts by experience work as equal partners throughout a research project, can improve the quality, relevance, implementation and impact of research. However, there is limited evidence on methods for successful co-production in data-intensive research with underserved groups. In partnership with the charity Voice of Young People in Care (VOYPIC) and a group of care experienced young people, the Administrative Data Research Centre Northern Ireland (ADRC NI) piloted and evaluated a co-production approach in a research project that used linked administrative data to examine the association between care experience and mental ill health and mortality. Objective: The aim of this paper is to report the impact of co-production using the pilot as a case study, and assess the mechanisms involved against published principles of co-production. Additionally, we consider if co-production in this context is a special case that warrants bespoke guidance. Methods: Two participatory workshops and three semi-structured 1-1 interviews were conducted to collect the perspectives of pilot participants. Deductive thematic analysis was used to sort data into three predetermined categories: 1) impact; 2) barriers; and 3) enablers. To formally assess pathways to implementing co-production and achieving impact, mechanisms were mapped against the five National Institute for Health and Care Research (NIHR) principles of co-production. Results: Positive impacts were identified for individuals, the research and organisations involved. Common barriers to co-production, like representativeness and resource constraints were identified, alongside challenges specific to data-intensive research, such as balancing power-sharing with data access constraints. Key enablers included genuine power sharing, valuing diverse knowledge, and partnership working. Special considerations needed to support successful co-production in this context include extra effort to achieve inclusion and address support needs. Partnerships with voluntary and community organisations support an inclusive, trauma-informed approach. Conclusion: This case study and evaluation can be utilised to support co-production with underserved groups in other data-intensive research contexts. Embedding co-production of data research with underserved groups will require changes to the broader research eco-system, including tailored guidance and resources, and funding partnerships rather than only pre-specified research projects.
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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 | 0.144 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".