Unpacking an affordance-based model of chronic pain: a video game analogy
Bibliographic record
Abstract
Abstract Chronic pain is one of the most disabling medical conditions globally, yet, to date, we lack a satisfying theoretical framework for research and clinical practice. Over the prior decades, several frameworks have been presented with biopsychosocial models as the most promising. However, in translation to clinical practice, these models are often applied in an overly reductionist manner, leaving much to be desired. In particular, they often fail to characterize the complexities and dynamics of the lived experience of chronic pain. Recently, an enactive, affordance-based approach has been proposed, opening up new ways to view chronic pain. This model characterizes how the persistence of pain alters a person’s field of affordances: the unfolding set of action possibilities that a person perceives as available to them. The affordance-based model provides a promising perspective on chronic pain as it allows for a systematic investigation of the interactive relation between patients and their environment, including characteristic alterations in the experience of their bodies and the space they inhabit. To help bridge the gap from philosophy to clinical practice, we unpack in this paper the core concepts of an affordance-based approach to chronic pain and their clinical implications, highlighting aspects that have so far received insufficient attention. We do so with an analogy to playing video games, as we consider such comparative illustration a useful tool to convey the complex concepts in an affordance-based model and further explore central aspects of the lived experience of chronic pain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".