What is Risk? Four Approaches to the Embodiment of Health Risk in Public Health
Bibliographic record
Abstract
Risk is a quotidian concept in public health, but there is little research on how ‘health risk’ is corporally experienced. In this article we apply sociological theories to analyse how a sample of 13 individuals made sense of embodied health risk. The data were collected through the adults sculpting, life-lining, and participating in interviews in Toronto, Canada in Autumn 2016. Through these activities we explored how participants related their embodied futures to their presents and pasts. Four approaches to how ‘health risk’ connects the body through time emerged from our analysis, focusing particularly on the interview data: the shit happens approach, the sequelae approach, the risk as heuristic approach, and the knowledge approach. These approaches elucidate how individuals make uncertain embodied futures stable through the different interpretations of the concept of risk. Our account of these four approaches builds on recent health risk research by providing individual, embodied accounts of risk that show how understandings of health risk connect individuals to broader systems. The four approaches have implications for considering pathways to achieving health justice, developing public health ethics, and understanding the role embodied risk plays in individual experiences of unequal health structures.
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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 | 0.029 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.012 | 0.149 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".