Patients, Public and Service Users are Experts by Experience: An Overview from Ophthalmology Research in Canada, UK and Beyond
Bibliographic record
Abstract
Discussion of the positive impact on research and mutual benefit that arises through genuine researcher and expert by experience collaboration has been noticeably absent from global sight loss and vision conferences. This article is co-authored by a parent advocate whose children have bilateral retinoblastoma, an eye health researcher and a practitioner in patient and public involvement in research who came together at the 2019 annual meeting of the Association for Research in Vision and Ophthalmology to share their first-hand experiences. The aim of this commentary is to highlight good practice and encourage colleagues to pursue steps towards a more engaged ophthalmology research landscape globally. Through living with conditions and/or engaging with health and social care services patients, public and service users become experts by experience. In Canada and the UK, the active involvement of experts by experience in ophthalmology research (as well as in other specialties) positively benefits all stages of the research cycle; improves the experience and outcomes for patients taking part in research; drives better engagement between researchers, the public and other key stakeholders; and benefits these expert’s own sense of wellbeing and achievement. At the moment, the extent to which experts by experience are active in ophthalmology research around the world is unclear, but likely to be minimal. To enable more research to benefit from the contribution of experts by experience, global efforts to improve the continuity and quality of reporting and evidence of impact 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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| 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".