Codesigning health and other public services with vulnerable and disadvantaged populations: Insights from an international collaboration
Bibliographic record
Abstract
BACKGROUND: Codesign has the potential to transform health and other public services. To avoid unintentionally reinforcing existing inequities, better understanding is needed of how to facilitate involvement of vulnerable populations in acceptable, ethical and effective codesign. OBJECTIVE: To explore citizens' involvement in codesigning public services for vulnerable groups, identify challenges and suggest improvements. DESIGN: A modified case study approach. Pattern matching was used to compare reported challenges with a priori theoretical propositions. SETTING AND PARTICIPANTS: A two-day international symposium involved 28 practitioners, academics and service users from seven countries to reflect on challenges and to codesign improved processes for involving vulnerable populations. INTERVENTION STUDIED: Eight case studies working with vulnerable and disadvantaged populations in three countries. RESULTS: We identified five shared challenges to meaningful, sustained participation of vulnerable populations: engagement; power differentials; health concerns; funding; and other economic/social circumstances. In response, a focus on relationships and flexibility is essential. We encourage codesign projects to enact a set of principles or heuristics rather than following pre-specified steps. We identify a set of principles and tactics, relating to challenges outlined in our case studies, which may help in codesigning public services with vulnerable populations. DISCUSSION AND CONCLUSIONS: Codesign facilitators must consider how meaningful engagement will be achieved and how power differentials will be managed when working with services for vulnerable populations. The need for flexibility and responsiveness to service user needs may challenge expectations about timelines and outcomes. User-centred evaluations of codesigned public services are needed.
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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 | 0.057 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.013 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.028 |
| Research integrity | 0.004 | 0.007 |
| 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".