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Record W3161702274 · doi:10.1080/13698575.2021.1929864

What is Risk? Four Approaches to the Embodiment of Health Risk in Public Health

2021· article· en· W3161702274 on OpenAlexafffundabout
Debra Kriger

Bibliographic record

VenueHealth Risk & Society · 2021
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsEmbodied cognitionPublic healthHealth riskFutures contractSociologyPsychologySocial psychologyEnvironmental healthEpistemologyMedicineNursingEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.040
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.576
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0400.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0070.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.386
GPT teacher head0.464
Teacher spread0.079 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations7
Published2021
Admission routes3
Has abstractyes

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