The public and patient involvement imperative in Ireland: Building on policy drivers
Bibliographic record
Abstract
What can we learn from the history of Public and Patient Involvement (PPI) in healthcare and research across global jurisdictions? Depending on region and context, the terminology and heritage of involvement in research vary. In this paper, we draw on global traditions to explore dominant themes and key considerations and critiques pertaining to PPI in order to inform a PPI culture shift in Ireland. We then describe the heritage of PPI in Ireland and present the case for combining methodological imperatives with policy drivers to support and encourage meaningful involvement. Specifically, we propose that PPI can be enriched by the theory and processes of participatory health research (PHR); and that implementation requires concurrent capacity building. We conclude with a call for Irish researchers (authors of this paper included) to consider the conceptual complexities and nuances of a participatory approach to build on the policy imperatives driving PPI and to contribute to the international evidence base and research culture. Specifically, we call for Irish health researchers and funders to consider and reflect on: (1) the rich literature of PHR as a resource for enacting meaningful PPI; (2) the roots and origins of varying participatory health research methods; (3) how community/patient groups can lead health research; and (4) co-learning and partnership synergy to create space for both academic and community expertise; and (5) the importance of using standardized reporting tools.
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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 | 0.231 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.084 |
| Scholarly communication | 0.043 | 0.036 |
| Open science | 0.005 | 0.043 |
| Research integrity | 0.013 | 0.031 |
| Insufficient payload (model declined to judge) | 0.004 | 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".