What Lawrence’s Story Tells Health Researchers About Arts-Based Interactions
Bibliographic record
Abstract
In arts-based-research, knowledge and meaning emerge from people's experiences of being in dynamic, ambiguous, intentional, and ethical relationships with each other and the arts.This case study draws on Launer's "7 C's" (context, conversations, curiosity, complexity, challenge, caution, and care) to understand the aesthetics (shape and form) and ethics of relationships between an artist-researcher and patient-sitter in portraiture-based medical research.This case supports the 7 C's being embodied in the art-making process, as the approach can usefully frame ethical challenges and rewards of portraiture-based health research for artist-researcher and patient-participant.Care Ethics of care (EoC) is a normative ethical framework that views moral action in terms of interpersonal relationships, with care and benevolence as core virtues.Because this ethical framework reflects the relationship between carers and patients, we decided to explore its usefulness for artist-sitter and researcher-participant relationships in a portraiture-based medical research (PBMR) study. 1 Launer has proposed that context, conversations, curiosity, complexity, challenge, caution, and care (the "7 C's") can help scholars understand the relationships between doctors and patients.2,3 Since many of these themes overlap with EoC, we decided to use Launer's 7 C's to explore the EoC in the interactions and relationships between the first author-Scottish artist-researcher, Mark Gilbert (M.G.)-and sitter-participant, Lawrence.This case report was drawn from the Giving, Receiving, Observing and Witnessing Care (GROWing Care) Study, which investigated the experience of older adults and their partners in care.4 We examined artworks, transcripts from conversations, and semi-structured interviews between artist-researcher and sitterparticipant, as well as artist journal reflections.Meeting Lawrence Present M.G. met Lawrence, then 91 years old, after Lawrence's appointment at a memory clinic for subjective memory complaints.Although Lawrence's test results showed no sign of
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.031 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".