Chronic Pain Beyond Measure: E-Health and the Politics of Pain Care
Bibliographic record
Abstract
This is a project about chronic pain. It explores the codes, contracts, and covenants that patients living with chronic pain enter as they try to feel better. What is unique about this study of chronic pain is the focus on e-health: the use of digital technologies in health. Therefore, more narrowly, this is a project about how the digitalization of healthcare impacts people living with chronic pain. From immersive serious games to symptom tracking applications to facial coding systems that detect pain via patterns of expression, digital technologies are increasingly touted as capable of solving long-standing challenges to treating people with chronic pain. However, these tools introduce novel issues and questions about chronic pain, technology, and justice in medicine. This dissertation describes the emergence of digitality in chronic pain medicine, the ideological backdrop of this paradigm, and how technoscientific practices impact the lives of people living with chronic pain in Canada. The findings of this research rely on qualitative data and theoretical analysis. Between 2022 and 2023, I conducted interviews with people living with chronic pain across Canada, a pain specialist, and the CEO of a popular pain tracking application. I also conducted participant observation at a multimodal chronic pain clinic. This data is theoretically triangulated at the intersection of science studies, philosophy, and critical feminist scholarship. However, research on pain within the humanities is extensive. Historical, anthropological, and arts-based work also informs the discussion and analysis.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.036 |
| Scholarly communication | 0.025 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".