Painful metaphors: enactivism and art in qualitative research
Bibliographic record
Abstract
Enactivism is an emerging theory for sense-making (cognition) with increasing applications to research and medicine. Enactivists reject the idea that sense-making is simply in the head or can be reduced to neural processes. Instead, enactivists argue that cognisers (people) are embodied and action-oriented, and that sense-making emerges from relational processes distributed across the brain-body-environment. We start this paper with an overview of a recently proposed enactive approach to pain. With rich theoretical and empirical roots in phenomenology and cognitive science, conceptualising pain as an enactive process is appealing as it overcomes the problematic dualist and reductionist nature of current pain theories and healthcare practices. Second, we discuss metaphor in the context of pain and enactivism, including a pain-related metaphor classification system. Third, we present and discuss five paintings created alongside an enactive study of clinical communication and the co-construction of pain-related meanings. Each painting represents pain-related metaphors delivered by clinicians during audio-recorded clinical appointments or discussed by clinicians and patients during interviews. We classify these metaphors, connecting them to enactive theory and relevant literature. The art, metaphors and associated narratives draw attention to the intertwined nature of language, meaning and pain. Of clinical relevance to primary and allied healthcare, we explore how clinicians’ taken-for-granted pain-related metaphors can act as scaffolding for patients’ pain and agency, for better or worse. We visually depict and give examples of clinical situations where metaphors became enactive, in that they were clinically reinforced and embodied through assessment and treatment. We conclude with research and clinical considerations, suggesting that enactive metaphor is a widely overlooked learning mechanism that clinicians could consider employing and intentionally shape.
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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 | 0.113 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.013 | 0.055 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".