Public Involvement in Cancer Research: Collaborative Evaluation Using Photovoice
Bibliographic record
Abstract
Background: A public involvement group consisting of 4 public contributors with lived experience of cancer diagnosis contributed to 2 cancer research projects that focused on optimizing the diagnostic pathways for patients with suspected cancer. The public contributors have been involved from the start of the projects and were involved in aspects of the design, analysis, and dissemination alongside research and clinical teams. Despite public involvement in cancer research being seen as a key element of the research process, there is still a limited understanding of what works well and how to do it in a meaningful way for both researchers and public contributors. Objective: This study aims to evaluate the public involvement process in 2 cancer research projects. Methods: This was a collaborative evaluation with the research team and public contributors jointly evaluating the process. Data were collected throughout the lifespan of the project by public contributors through photovoice, where they collected photos that represented their experiences of involvement. At the end of the evaluation meeting, 2 separate analyses were conducted. First, public contributors reflected on their experiences using a 4-dimensional framework to capture how strong their voice was, how many ways they had an opportunity to be involved, if their feedback was implemented, and if the discussion focused on their priorities. Second, they analyzed the collected photos by organizing them alongside their narratives, explaining their meanings and comparing how they experienced the involvement process. Results: Narratives from 8 photos illustrate public contributors' experience of involvement in these projects, presenting them in chronological order, showing how their perspectives evolved from not knowing what form the project would take, through understanding foundations and building confidence through being satisfied with the successful projects. Results from the 4-dimensional framework showed that public contributors felt that their voices were strong, and the research and clinical team mostly implemented suggested changes. The discussion focused on topics and issues that were relevant to public contributors. However, how public contributors were involved depended mainly on the research team's decision, and they would have preferred more opportunities. Conclusions: This study has shown that public contributors can be meaningfully involved throughout the lifespan of cancer research projects. The evaluation demonstrated that establishing a strong relationship and trust between researchers and public contributors helps to ensure that the public contributors' voice is meaningful and makes a difference in the projects. However, it also identified improvements for future public involvement. Researchers should involve public contributors as early as the funding application stage to offer more opportunities to shape research and thus have diverse involvement opportunities at each stage of the research process.
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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 | 0.016 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".