Re-conceptualizing ‘impact’ in art-based health research
Bibliographic record
Abstract
Abstract This article explores the notion of ‘impact’ in art-based health research (ABHR), and how we might re-conceptualize it through the kind of work ABHR ‘does’ in generating and disseminating knowledge. We explore ‘impact’ from a critical qualitative perspective, leveraging findings from a study based on interviews with ABHR researchers/artists/trainees. We focus on their reflections related to ‘impact’, and informed by our own experiences of producing/evaluating ABHR in diverse genres. We argue for a conceptualization of impact that moves beyond an exclusive positivist and biomedical concern with whether certain ABHR ‘interventions’ (defined here as processes/products of an ABHR study) work in generalizable ways, to one that focuses on context as well as processes of development, implementation and engagement. How will we know if a particular ABHR project ‘worked’? What kinds of ‘work’ do the products of ABHR do? How might we, or should we, tease out ‘process’ from ‘product’? In exploring these questions, we problematize what is meant by ‘impact’ and what we can expect from the knowledge generated and its translation via varied artistic genres.
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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 | 0.122 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.009 | 0.173 |
| Scholarly communication | 0.028 | 0.030 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.007 | 0.011 |
| 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".