Abstracts from the NIHR INVOLVE Conference 2017
Bibliographic record
Abstract
AimsDuring this presentation we will share learning from a Wellcome Trust Engagement Fellowship.We will present examples of artsbased public involvement activities, including a sculpture project with young people and a play about dementia.We aim to raise awareness of what public involvement can gain from the arts; stimulate discussion about the pros and cons of different approaches; and discuss how to encourage more creativity within public involvement.Why is it important and to whom?Public involvement has been criticised for a lack of diversity and inclusivity.By diversifying the involvement activities which we offer, we may attract a wider variety of people.Arts based activities also have the potential to facilitate discussion in an accessible, safe and fun way.This session may be of particular interest to people who are planning or facilitating public involvement activities (members of the public and researchers).What difference has, or could, this project make?Throughout the project, both researchers and members of the public have found arts activities stimulating and useful.However people have encountered some practical challenges when running these projects.Specifically, people do not feel they have the necessary skills to plan and facilitate arts activities.I will discuss how we might address that skills gap and invite the audience to suggest what support is needed. What will people take away from session?An understanding of what arts/health collaborations can offer public involvement Access to resources and contacts to support future projects
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.450 | 0.180 |
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".