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Record W4308867382 · doi:10.3389/fpubh.2022.1038409

The public and patient involvement imperative in Ireland: Building on policy drivers

2022· article· en· W4308867382 on OpenAlexfundno aff
Meghan Gilfoyle, Anne MacFarlane, Ailish Hannigan, Vikram Niranjan, Zoe Hughes, Jon Salsberg

Bibliographic record

VenueFrontiers in Public Health · 2022
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchHealth Research Board
KeywordsGeneral partnershipPublic relationsTerminologyContext (archaeology)Participatory action researchCitizen journalismIrishPublic healthConceptual frameworkKnowledge managementHealth careSociologyPolitical scienceMedicineComputer scienceNursingSocial science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.788
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.103
GPT teacher head0.385
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2022
Admission routes1
Has abstractyes

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