Embracing Metaphor in Pain Medicine. Forthcoming in the Routledge Handbook of Medicine and Poetry edited by Alan Bleakley and Shane Neilson.
Bibliographic record
Abstract
There is a widespread assumption that medicine should be objective, using standardized terminology and plain speech to relay raw facts about disease and health (Bleakley 2017). Within this paradigm, figurative language, such as metaphor, is viewed as unnecessary and to be avoided. Yet, the blind spot of this view is that medicine, science, and human languages are built on a foundation of metaphor (Lakoff and Johnson 1980, Bleakley 2017). In this chapter, we focus on the use of metaphor in clinical practice, specifically pain management. Drawing from our own and others’ theoretical and empirical work, we argue that metaphor is essential, unavoidable, and potentially quite helpful. Rather than attempting to sanitize clinical practice by suppressing metaphor use, there is a need to intentionally and carefully co-construct metaphors with people living with pain to facilitate mutual understandings and enhance health-related outcomes (Neilson 2016, Bleakley 2017, Stilwell et al. 2021). Overall, this chapter represents a call to embrace metaphor in medicine to improve communication about the idiosyncratic complexities of pain, and to avoid overly simplistic messages that may do more harm than good.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".